225 results on '"heterogeneous information networks"'
Search Results
2. Type-adaptive graph Transformer for heterogeneous information networks.
- Author
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Tang, Yuxin, Huang, Yanzhe, Hou, Jingyi, and Liu, Zhijie
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GRAPH neural networks ,TRANSFORMER models ,REPRESENTATIONS of graphs ,INFORMATION networks - Abstract
Many real-world applications use diverse types of nodes and edges to retain rich semantic information. These applications are modeled as heterogeneous graphs. Recent research on heterogeneous graph embedding has made great progress because of the powerful ability of graph neural networks (GNNs) to capture the structural information of graphs. However, the performance of existing heterogeneous graph neural networks (HGNNs) is still unsatisfactory because 1) the aggregation and update functions of GNNs do not exploit the types of nodes and edges, which provide task-relevant information in heterogeneous information networks (HINs), and 2) message-passing-based GNNs are limited by oversmoothing and oversquashing, which prevents the central node from obtaining information from its higher-order neighbors. In this paper, we propose a type-adaptive graph Transformer (Tagformer) that considers not only local structure information and higher-order neighbor information in HINs but also type information to improve performance across various downstream tasks. Specifically, Tagformer assigns each node with the corresponding type feature and uses a GNN and graph Transformer (GT) to extract local structure information and higher-order neighbor information, respectively. Furthermore, to reduce the quadratic complexity and eliminate irrelevant information, we design an intraclass pooling module to condense the large-scale nodes of a graph into a reduced set of pooling nodes. We conduct extensive experiments on four HIN benchmark datasets, demonstrating that Tagformer consistently outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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- 2024
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3. A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions
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Bo-Wei Zhao, Xiao-Rui Su, Yue Yang, Dong-Xu Li, Guo-Dong Li, Peng-Wei Hu, Xin Luo, and Lun Hu
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Network structural representation ,Heterogeneous information networks ,Biological and network representations ,LMIs ,Biotechnology ,TP248.13-248.65 - Abstract
Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs). However, most of them complete their tasks by mainly focusing on a single lncRNA-miRNA network without considering the complex mechanism between biomolecular in life activities, which are believed to be useful for improving the accuracy of LMI prediction. To address this, a heterogeneous information network (HIN) learning model with neighborhood-level structural representation, called HINLMI, to precisely identify LMIs. In particular, HINLMI first constructs a HIN by integrating nine interactions of five biomolecules. After that, different representation learning strategies are applied to learn the biological and network representations of lncRNAs and miRNAs in the HIN from different perspectives. Finally, HINLMI incorporates the XGBoost classifier to predict unknown LMIs using final embeddings of lncRNAs and miRNAs. Experimental results show that HINLMI yields a best performance on the real dataset when compared with state-of-the-art computational models. Moreover, several analysis experiments indicate that the simultaneous consideration of biological knowledge and network topology of lncRNAs and miRNAs allows HINLMI to accurately predict LMIs from a more comprehensive perspective. The promising performance of HINLMI also reveals that the utilization of rich heterogeneous information can provide an alternative insight for HINLMI to identify novel interactions between lncRNAs and miRNAs.
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- 2024
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4. Predicting gene-disease associations using a heterogeneous graph neural network
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Denis A. Sidorenko and Anatoly A. Shalyto
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machine learning ,graph neural networks ,heterogeneous information networks ,bioinformatics ,genetics ,“gene-disease” prediction associations ,Optics. Light ,QC350-467 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
The research presents the development of a heterogeneous graph neural network model for predicting gene-disease using existing genomic and medical data. The novelty of the approach is in integrating the principles of graph neural networks and heterogeneous information networks for efficient processing of structured data and consideration of complex genepathology interactions. The solution proposed is a heterogeneous graph neural network which utilizes a heterogeneous graph structure for representing genes, diseases, and their relationships. The performance of the developed model was evaluated on the DisGeNET, LASTFM, YELP datasets. On these datasets, a comparison was made with current SOTA models. The comparison results demonstrated that the proposed model outperforms other models in terms of Average Precision (AP), F1-measure (F1@S), Hit@k, Area Under Receiver Operating Characteristic curve (AUROC) in predicting “gene-disease” associations. The model developed serves as a tool for bioinformatics analysis and can aid researchers and doctors in studying genetic diseases. This could expedite the discovery of new drug targets and the advancement of personalized medicine.
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- 2024
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5. DyHANE: dynamic heterogeneous attributed network embedding through experience node replay
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Liliana Martirano, Dino Ienco, Roberto Interdonato, and Andrea Tagarelli
- Subjects
Graph Continual Learning ,Heterogeneous Information Networks ,Incremental Graph Neural Networks ,Applied mathematics. Quantitative methods ,T57-57.97 - Abstract
Abstract With real-world network systems typically comprising a large number of interactive components and inherently dynamic, Graph Continual Learning (GCL) has gained increasing popularity in recent years. Furthermore, most applications involve multiple entities and relationships with associated attributes, which has led to widely adopting Heterogeneous Information Networks (HINs) for capturing such rich structural and semantic meaning. In this context, we deal with the problem of learning multi-type node representations in a time evolving graph setting, harnessing the expressive power of Graph Neural Networks (GNNs). To this purpose, we propose a novel framework, named DyHANE—Dynamic Heterogeneous Attributed Network Embedding, which dynamically identifies a representative sample of multi-typed nodes as training set and updates the parameters of a GNN module, enabling the generation of up-to-date representations for all nodes in the network. We show the advantage of employing HINs on a data-incremental classification task. We compare the results obtained by DyHANE on a multi-step, incremental heterogeneous GAT model trained on a sample of changed and unchanged nodes, with the results obtained by either the same model trained from scratch or the same model trained solely on changed nodes. We demonstrate the effectiveness of the proposed approach in facing two major related challenges: (i) to avoid model re-train from scratch if only a subset of the network has been changed and (ii) to mitigate the risk of losing established patterns if the new nodes exhibit unseen properties. To the best of our knowledge, this is the first work that deals with the task of (deep) graph continual learning on HINs.
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- 2024
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6. SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach.
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Guo, Yiyang and Zhou, Zheyu
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CONVOLUTIONAL neural networks , *BIG data , *INDIVIDUALIZED instruction , *INFORMATION overload , *RECOMMENDER systems , *SOCIAL media , *SOCIAL networks - Abstract
In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students' unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Robust Local Community Search over Large Heterogeneous Information Networks
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Li, Yuan, Kong, Qingxin, Song, Wei, Yang, Guoli, Zhao, Yuhai, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhang, Wenjie, editor, Tung, Anthony, editor, Zheng, Zhonglong, editor, Yang, Zhengyi, editor, Wang, Xiaoyang, editor, and Guo, Hongjie, editor
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- 2024
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8. Disentangled Hierarchical Attention Graph Neural Network for Recommendation
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He, Weijie, Ouyang, Yuanxin, Peng, Keqin, Rong, Wenge, Xiong, Zhang, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Huang, De-Shuang, editor, Zhang, Xiankun, editor, and Zhang, Qinhu, editor
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- 2024
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9. Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks
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Yu, Yiwei, Zhou, Lihua, Liu, Chao, Wang, Lizhen, Chen, Hongmei, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meng, Xiaofeng, editor, Zhang, Xueying, editor, Guo, Danhuai, editor, Hu, Di, editor, Zheng, Bolong, editor, and Zhang, Chunju, editor
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- 2024
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10. Research on Joint Recommendation Algorithm for Knowledge Concepts and Learning Partners Based on Improved Multi-Gate Mixture-of-Experts.
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Shou, Zhaoyu, Chen, Yixin, Wen, Hui, Liu, Jinghua, Mo, Jianwen, and Zhang, Huibing
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CONCEPT learning ,MASSIVE open online courses ,LEARNING ,ONLINE education ,HUMAN-computer interaction - Abstract
The rise of Massive Open Online Courses (MOOCs) has increased the large audience for higher education. Different learners face different learning difficulties in the process of online learning. In order to ensure the quality of teaching, online learning resource recommendation services should be more personalised and have more choices. In this paper, we propose a joint recommendation algorithm for knowledge concepts and learning partners based on improved MMoE (Multi-gate Mixture-of-Experts). Firstly, the heterogeneous information network (HIN) is constructed based on the MOOC platform and appropriate meta-paths are selected in order to extract the human–computer interaction information and student–student interaction information generated during the learners' online learning processes more completely. Secondly, the temporal behavioural characteristics of students are obtained based on their learning paths as well as their knowledge of conceptual characteristics, and LSTM (Long Short-Term Memory) is used to mine students' current learning interests. Finally, the gating network in MMoE is changed into an attention mechanism network, and for different tasks, multiple attention mechanism networks are used to fuse the learner's human–computer interaction information, student–student interaction information, and interest characteristics to generate learner representations that are more in line with the respective task and to complete the tasks of knowledge conception and learning partner recommendation. Experiments on publicly available MOOC datasets show that the method proposed in this paper provides more accurate and varied personalization services to online learners compared to the latest proposed methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. GSL-DTI: Graph structure learning network for Drug-Target interaction prediction.
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E, Zixuan, Qiao, Guanyu, Wang, Guohua, and Li, Yang
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DEEP learning , *DRUG discovery , *DRUG interactions , *MACHINE learning , *BASE pairs , *DRUG development - Abstract
• We propose an automated end-to-end graph structure learning model, GSL-DTI, for Drug-Target Interaction (DTI) prediction. In contrast to previous studies relying on manual rules, our approach incorporates an automatic graph structure learning method, which utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. • We conduct experiments on three public datasets and compare our method against competitive baselines. The experimental results demonstrate a significant outperformance of our model over state-of-the-art methods. • Furthermore, the introduction of graph structure learning offers a fresh perspective for DTI prediction research. To the best of our knowledge, GSL-DTI represents the first attempt to apply automatic graph structure learning to DTI tasks. It reduces the reliance on expert knowledge and yields improved node representations for downstream tasks. Motivation: Drug-target interaction prediction is an important area of research to predict whether there is an interaction between a drug molecule and its target protein. It plays a critical role in drug discovery and development by facilitating the identification of potential drug candidates and expediting the overall process. Given the time-consuming, expensive, and high-risk nature of traditional drug discovery methods, the prediction of drug-target interactions has become an indispensable tool. Using machine learning and deep learning to tackle this class of problems has become a mainstream approach, and graph-based models have recently received much attention in this field. However, many current graph-based Drug-Target Interaction (DTI) prediction methods rely on manually defined rules to construct the Drug-Protein Pair (DPP) network during the DPP representation learning process. However, these methods fail to capture the true underlying relationships between drug molecules and target proteins. We propose GSL-DTI, an automatic graph structure learning model used for predicting drug-target interactions (DTIs). Initially, we integrate large-scale heterogeneous networks using a graph convolution network based on meta -paths, effectively learning the representations of drugs and target proteins. Subsequently, we construct drug-protein pairs based on these representations. In contrast to previous studies that construct DPP networks based on manual rules, our method introduces an automatic graph structure learning approach. This approach utilizes a filter gate on the affinity scores of DPPs and relies on the classification loss of downstream tasks to guide the learning of the underlying DPP network structure. Based on the learned DPP network, we transform the prediction of drug-target interactions into a node classification problem. The comprehensive experiments conducted on three public datasets have shown the superiority of GSL-DTI in the tasks of DTI prediction. Additionally, GSL-DTI provides a fresh perspective for advancing research in graph structure learning for DTI prediction. [ABSTRACT FROM AUTHOR]
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- 2024
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12. 基于异构信息网络的 Android 恶意程序检测方法.
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殷丹丽 and 凌 捷
- Abstract
Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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13. REHREC: Review Effected Heterogeneous Information Network Recommendation System
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Farhad Khalilzadeh and Ilyas Cicekli
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Heterogeneous information networks ,recommendation systems ,network embedding ,meta-path base random walk ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Heterogeneous Information Networks have bunches of rich secret information that assist us in the creation of successful recommendation frameworks. A Heterogeneous Information Network (HIN) includes useful knowledge required for a recommendation system, and the network embedding is the common strategy for getting useful knowledge out of a HIN to be used in recommendation platforms. Although user and business nodes have been used in HINs, review contents have not been used. In this work, we use review nodes in HINs in addition to user and business nodes. Since written reviews provide valuable insights into points of interest within recommendation systems, integrating review nodes into HINs allows us to assess their impact on recommendation systems. Specifying meaningful meta-paths aids in extracting hidden information within a heterogeneous information network. While user and business nodes are typically utilized for specifying meaningful meta-paths, review nodes have been overlooked. We introduce new meta-paths incorporating review nodes to uncover hidden information in heterogeneous information networks. These meta-paths are leveraged to enhance the recommendation system’s performance. This study endeavors to amalgamate rich written reviews with heterogeneous information networks and analyze their effects on recommendation systems. Our experiments demonstrate that incorporating review texts improves the recommendation system, particularly when selecting meaningful meta-paths. Augmenting HINs with reviews facilitates the capture of additional relational information between users and businesses, thereby enhancing the recommendation model. This underscores the benefits of consolidating interaction information within HIN features for superior recommendation outcomes.
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- 2024
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14. MECCH: Metapath Context Convolution-based Heterogeneous Graph Neural Networks.
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Fu, Xinyu and King, Irwin
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RESEARCH personnel , *REPRESENTATIONS of graphs , *GRAPH algorithms - Abstract
Heterogeneous graph neural networks (HGNNs) were proposed for representation learning on structural data with multiple types of nodes and edges. To deal with the performance degradation issue when HGNNs become deep, researchers combine metapaths into HGNNs to associate nodes closely related in semantics but far apart in the graph. However, existing metapath-based models suffer from either information loss or high computation costs. To address these problems, we present a novel Metapath Context Convolution-based Heterogeneous Graph Neural Network (MECCH). MECCH leverages metapath contexts , a new kind of graph structure that facilitates lossless node information aggregation while avoiding any redundancy. Specifically, MECCH applies three novel components after feature preprocessing to extract comprehensive information from the input graph efficiently: (1) metapath context construction, (2) metapath context encoder, and (3) convolutional metapath fusion. Experiments on five real-world heterogeneous graph datasets for node classification and link prediction show that MECCH achieves superior prediction accuracy compared with state-of-the-art baselines with improved computational efficiency. The code is available at https://github.com/cynricfu/MECCH. • Unified a framework for metapath-based HGNNs and analyzed their limitations. • Introduced metapath contexts for lossless and efficient node information aggregation. • Proposed MECCH that leverages metapath contexts with better efficacy and efficiency. [ABSTRACT FROM AUTHOR]
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- 2024
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15. A node clustering algorithm for heterogeneous information networks based on node embeddings.
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Liu, Dongjiang and Li, Leixiao
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Clustering is a very important method to analyze HIN. Thus, several HIN clustering algorithms have been proposed and all these algorithms are based on meta-paths. Meta-path can be used to describe the relationship of target objects. Even though the relationship of target objects are fully considered by these meta-path based algorithms, the directly connected neighbors of target objects are neglected by them. These neglected directly connected neighbors are not target objects, but they contain plenty of useful information for finding clusters of target objects. So, while performing clustering based on HIN, these neglected neighbors should be considered. To achieve the goal, in this paper, a new HIN clustering algorithm is proposed. The proposed algorithm tries to build a vector for each target object. The clustering task is fulfilled based on these vectors. During the vector building process, the neighbors of all the target objects are considered. As clustering result of HIN is affected by different factors, such as neighbors of target objects and different kinds of meta-paths, several similarity matrices are built in the proposed algorithm. Each matrix is corresponding to a specific factor. Besides, every matrix will be assigned a weight value. These weight values are used to represent the relative importance of factors. At the same time, in the proposed algorithm, a new training method is adopted to calculate the vectors and the weight values. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Drug-disease association prediction using semantic graph and function similarity representation learning over heterogeneous information networks.
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Zhao, Bo-Wei, Su, Xiao-Rui, Yang, Yue, Li, Dong-Xu, Li, Guo-Dong, Hu, Peng-Wei, Zhao, Yong-Gang, and Hu, Lun
- Subjects
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INFORMATION networks , *DRUG repositioning , *LEARNING strategies , *RANDOM forest algorithms , *BIOLOGICAL networks , *SEMANTICS - Abstract
Discovering new indications for existing drugs is a promising development strategy at various stages of drug research and development. However, most of them complete their tasks by constructing a variety of heterogeneous networks without considering available higher-order connectivity patterns in heterogeneous biological information networks, which are believed to be useful for improving the accuracy of new drug discovering. To this end, we propose a computational-based model, called SFRLDDA, for drug-disease association prediction by using semantic graph and function similarity representation learning. Specifically, SFRLDDA first integrates a heterogeneous information network (HIN) by drug-disease, drug-protein, protein-disease associations, and their biological knowledge. Second, different representation learning strategies are applied to obtain the feature representations of drugs and diseases from different perspectives over semantic graph and function similarity graphs constructed, respectively. At last, a Random Forest classifier is incorporated by SFRLDDA to discover potential drug-disease associations (DDAs). Experimental results demonstrate that SFRLDDA yields a best performance when compared with other state-of-the-art models on three benchmark datasets. Moreover, case studies also indicate that the simultaneous consideration of semantic graph and function similarity of drugs and diseases in the HIN allows SFRLDDA to precisely predict DDAs in a more comprehensive manner. • Different representation learning strategies are developed to learn the semantic graph and function similarity representations of drugs and diseases. • SFRLDDA, a potent drug repositioning algorithm, is introduced to accurately discern novel DDAs by learning multifaceted representations of drugs and diseases. • Experimental results demonstrate that SFRLDDA yields a best performance when compared with several state-of-the-art drug repositioning algorithms under ten-fold cross-validation. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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17. Weight normalization optimization movie recommendation algorithm based on three-way neural interaction networks.
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Liang, Zhenlu, Yang, Zhisheng, and Cheng, Jingyong
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CONVOLUTIONAL neural networks ,INFORMATION networks ,STOCHASTIC convergence ,ALGORITHMS - Abstract
Heterogeneous information networks are increasingly used in recommendation algorithms. However, they lack an explicit representation of meta-paths. In using bidirectional neural interaction models for recommendation models, interaction between users and items is often ignored, with an integral impact on the accuracy of the recommendations. To better apply the interaction information, this study proposes a weight-normalized movie recommendation model (SCLW_MCRec) based on a three-way neural interaction network. The model constructs a three-way neural interaction network ⟨ user, meta-path, item ⟩ from meta-path contextual information, introducing meta-paths on top of the user-item representation to represent the user-item interaction information. Introduction of a two-layer, one-dimensional convolutional neural network helps capture higher-order interaction features between the user and the item, making the model more powerful in terms of interaction. Adding a dropout layer to the interaction model and using a two-layer convolutional neural network can prevent overfitting and discard irrelevant information features to improve the recommendation. In addition, an extreme cross-entropy loss (argmaxminloss) that incorporates the properties of the argmin and argmax functions is designed to reduce the model loss. A weight-normalization optimization approach is used to better optimize the model and accelerate convergence of the stochastic gradient descent optimization. Compared to current state-of-the-art recommendation models, the SCLW_MCRec model improves the Prec evaluation index by 2.94–35.8%, Recall by 1.15–53.51%, and NDCG by 6.7–49.37% on the MovieLens dataset. The framework provides a significant improvement in recommendation accuracy and also solves the cold-start problem with application of interaction information. [ABSTRACT FROM AUTHOR]
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- 2023
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18. UKGAT: Uncertain Knowledge Graph Embedding Enriched KGAT for Recommendation
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Leng, Quanyang, Jiang, Wanting, Guo, Nan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Yang, Xiaochun, editor, Suhartanto, Heru, editor, Wang, Guoren, editor, Wang, Bin, editor, Jiang, Jing, editor, Li, Bing, editor, Zhu, Huaijie, editor, and Cui, Ningning, editor
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- 2023
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19. Link Prediction-Based Multi-Identity Recognition of Darknet Vendors
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Zou, Futai, Hu, Yuelin, Xu, Wenliang, Wu, Yue, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Wang, Ding, editor, Liu, Zheli, editor, and Chen, Xiaofeng, editor
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- 2023
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20. Leveraging Semantic Relationships to Prioritise Indicators of Compromise in Additive Manufacturing Systems
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Kumar, Mahender, Epiphaniou, Gregory, Maple, Carsten, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Zhou, Jianying, editor, Batina, Lejla, editor, Li, Zengpeng, editor, Lin, Jingqiang, editor, Losiouk, Eleonora, editor, Majumdar, Suryadipta, editor, Mashima, Daisuke, editor, Meng, Weizhi, editor, Picek, Stjepan, editor, Rahman, Mohammad Ashiqur, editor, Shao, Jun, editor, Shimaoka, Masaki, editor, Soremekun, Ezekiel, editor, Su, Chunhua, editor, Teh, Je Sen, editor, Udovenko, Aleksei, editor, Wang, Cong, editor, Zhang, Leo, editor, and Zhauniarovich, Yury, editor
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- 2023
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21. Influential Community Search Over Large Heterogeneous Information Networks
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Li, Xingyu, Zhou, Lihua, Kong, Bing, Wang, Lizhen, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Meng, Xiaofeng, editor, Li, Xiang, editor, Xu, Jianqiu, editor, Zhang, Xueying, editor, Fang, Yuming, editor, Zheng, Bolong, editor, and Li, Yafei, editor
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- 2023
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22. A GCN-Based Sequential Recommendation Model for Heterogeneous Information Networks
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Li, Dong, Wang, Yuchen, Chen, Tingwei, Sun, Xijun, Wang, Kejian, Wu, Gaokuo, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yang, Shiyu, editor, and Islam, Saiful, editor
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- 2023
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23. Research and strategy of university education and teaching reform based on intelligent education platform
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Zhang Juan
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heterogeneous information networks ,knowledge graphs ,meta-paths ,smart education and platforms ,97d60 ,Mathematics ,QA1-939 - Abstract
Exploring the path of college education and teaching reform is the way to improve the quality of college teaching and promote the development of college education. Based on big data technology, this paper proposes a wisdom education cloud platform, which is used to realize the comprehensive utilization of teaching resources for users. Based on the personalized teaching resource recommendation demand of users, a recommendation algorithm of high-order preference propagation based on a heterogeneous knowledge graph is jointly constructed by using a meta-path and knowledge graph based on a heterogeneous information network so as to realize the personalized recommendation of the platform to users and simulate and analyze the platform. In terms of platform performance, the random write and sequential write rates of the platform reach 4765 rows/s and 3218 rows/s, respectively, and the data transmission time is reduced by 68.35% and 69.35% compared with that of NetEase Cloud Classroom and Tencent Classroom, respectively. In terms of satisfaction, the platform in this paper has improved by 5.41% and 6.7% compared with NetEase Cloud Classroom and Tencent Classroom, respectively. This shows that the intelligent education cloud platform can be the exploration direction of education teaching reform in colleges and universities.
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- 2024
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24. Research on the structure of corporate financial management objective system based on BRP method
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Han Baozhen
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intelligent financial management ,heterogeneous information networks ,bayesian ranking ,dsbpr model ,68t05 ,Mathematics ,QA1-939 - Abstract
Exploring the architecture of corporate financial management objectives of the BRP method is to help companies achieve revenue growth. In this paper, we analyze the motivation for implementing business process reengineering and constructing an intelligent financial management system based on the BRP method and intelligent technology. The DSBPR data analysis and processing model is constructed by using Bayesian ranking model combined with similarity in heterogeneous information networks in the intelligent financial management system. The intelligent financial management system constructed in this paper is analyzed in terms of the operating capacity and profitability of the enterprise. From the viewpoint of operating capability, the turnover rate of receivables, total assets, and current assets decreased by 87.17%, 91.85% and 49.68%, respectively in ten years, and the change rate of inventory turnover was 7.91%. In terms of profitability, there is a difference of 166.12% between the maximum and minimum values of return on net assets. This shows that the use of an intelligent financial management system can provide intuitive data analysis for enterprise development, which helps enterprises to target the coordinated development of enterprise strategies and thus promote the improvement of economic returns.
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- 2024
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25. DyHANE: dynamic heterogeneous attributed network embedding through experience node replay
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Martirano, Liliana, Ienco, Dino, Interdonato, Roberto, and Tagarelli, Andrea
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- 2024
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26. Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs.
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Li, Jianfeng and Yang, Dexiang
- Subjects
FRAUD in science ,BIPARTITE graphs ,FRAUD investigation ,FRAUD ,FINANCIAL research ,RELATIONAL databases ,GRAPH algorithms ,BUILDING information modeling - Abstract
The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user's neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining. [ABSTRACT FROM AUTHOR]
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- 2023
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27. Heterogeneous Network Embedding: A Survey.
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Sufen Zhao, Rong Peng, Po Hu, and Liansheng Tan
- Subjects
INFORMATION networks ,RECOMMENDER systems ,INFORMATION retrieval ,MACHINE learning - Abstract
Real-world complex networks are inherently heterogeneous; they have different types of nodes, attributes, and relationships. In recent years, variousmethods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks (HINs) into low-dimensional embeddings; this task is called heterogeneous network embedding (HNE). Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification, recommender systems, and information retrieval. Here, we provide a comprehensive survey of key advancements in the area of HNE. First, we define an encoder-decoder-based HNE model taxonomy. Then, we systematically overview, compare, and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks. We also summarize the application fields, benchmark datasets, open source tools, and performance evaluation in theHNEarea. Finally, we discuss open issues and suggest promising future directions. We anticipate that this survey will provide deep insights into research in the field of HNE. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
28. Metacognition-driven user-to-project recommendation for online education services.
- Author
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Wu, Zezheng, Jiang, Haonan, Cheng, Xinghe, Huang, Haotian, Yang, Qing, and Zhang, Jingwei
- Subjects
- *
ONLINE education , *RECOMMENDER systems , *INFORMATION filtering , *RANDOM walks , *EDUCATIONAL outcomes , *VIRTUAL communities , *FILTERS & filtration - Abstract
Various online learning platforms have accumulated a large number of users who are learning or have completed their studies. In which, online education mostly provides services for theoretical learning. A vital issue is how to bridge theoretical learning and practical projects for improving users' competence. To address this issue, we propose a novel M etacognition-driven U ser-to-P roject recommendation approach for online E ducation S ervices (MUP-ES). We closely model theoretical content and practical project by constructing H eterogeneous I nformation N etworks (HINs) covering both user learning outcomes (learned courses, acquired knowledge concepts, etc.) and project knowledge requirements. We design an attention-based metacognitive information aggregation process to efficiently refine the structural and semantic information of HINs and match users to appropriate projects. To embed HINs, we design a meta-path based random walk strategy to generate meaningful node sequences. MUP-ES provides two major components, path filtering and information aggregation. The path filtering module filters the impurity information in the path through the attention mechanism, and enhances the weight of user learning information that is more suitable for the current project. The information aggregation module learns the neighborhood information of the nodes in the path through the multi-head self-attention mechanism, which aggregates the rich user-related information to the project nodes. Finally, MUP-ES predicts the project's match score with real users and tests the model's ability to mitigate the cold-start problem. Extensive experiments on two real-world education datasets show that MUP-ES achieves more accurate prediction results than state-of-the-art baselines. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
29. Link prediction for heterogeneous information networks based on enhanced meta-path aggregation and attention mechanism.
- Author
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Shao, Hao, Wang, Lunwen, and Zhu, Rangang
- Abstract
Heterogeneous link prediction aims to reveal potential connections between two nodes in heterogeneous information networks. Most existing studies are based on meta-paths, but ignore the information contained in incomplete meta-paths. They simply aggregate meta-paths, leading to mining semantic information insufficiently. To solve this problem, we propose a link prediction model based on enhanced meta-path aggregation and attention mechanism. In this model, the deficiency of missing topological information from incomplete meta-paths is compensated by aggregating structural features and semantics. Different from existing meta-path encoders, we use recurrent neural networks and the attention mechanism to learn explicit and implicit semantic knowledge from meta-paths, which can capture more complex semantic associations between nodes. In addition, to avoid duplicate feature acquisition by random walking, we design a novel bidirectional biased random walking algorithm. It is applied to guide the generation of heterogeneous neighbors of each node that contain features ignored by the meta-path-wise model, which can mine complete topological information and get more accurate link prediction results. The extensive experiments on several datasets demonstrate that the proposed model outperforms baselines. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
30. Weight normalization optimization movie recommendation algorithm based on three-way neural interaction networks
- Author
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Zhenlu Liang, Zhisheng Yang, and Jingyong Cheng
- Subjects
Heterogeneous information networks ,Recommender systems ,Three-way neural interaction ,Weight normalization optimization ,Electronic computers. Computer science ,QA75.5-76.95 ,Information technology ,T58.5-58.64 - Abstract
Abstract Heterogeneous information networks are increasingly used in recommendation algorithms. However, they lack an explicit representation of meta-paths. In using bidirectional neural interaction models for recommendation models, interaction between users and items is often ignored, with an integral impact on the accuracy of the recommendations. To better apply the interaction information, this study proposes a weight-normalized movie recommendation model (SCLW_MCRec) based on a three-way neural interaction network. The model constructs a three-way neural interaction network $$\langle $$ ⟨ user, meta-path, item $$\rangle $$ ⟩ from meta-path contextual information, introducing meta-paths on top of the user-item representation to represent the user-item interaction information. Introduction of a two-layer, one-dimensional convolutional neural network helps capture higher-order interaction features between the user and the item, making the model more powerful in terms of interaction. Adding a dropout layer to the interaction model and using a two-layer convolutional neural network can prevent overfitting and discard irrelevant information features to improve the recommendation. In addition, an extreme cross-entropy loss (argmaxminloss) that incorporates the properties of the argmin and argmax functions is designed to reduce the model loss. A weight-normalization optimization approach is used to better optimize the model and accelerate convergence of the stochastic gradient descent optimization. Compared to current state-of-the-art recommendation models, the SCLW_MCRec model improves the Prec evaluation index by 2.94–35.8%, Recall by 1.15–53.51%, and NDCG by 6.7–49.37% on the MovieLens dataset. The framework provides a significant improvement in recommendation accuracy and also solves the cold-start problem with application of interaction information.
- Published
- 2023
- Full Text
- View/download PDF
31. SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach
- Author
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Yiyang Guo and Zheyu Zhou
- Subjects
information recommendation ,social recommendation ,heterogeneous information networks ,social network analysis ,contrastive learning ,Mathematics ,QA1-939 - Abstract
In an era overwhelmed by academic big data, students grapple with identifying academic papers that resonate with their learning objectives and research interests, due to the sheer volume and complexity of available information. This study addresses the challenge by proposing a novel academic paper recommendation system designed to enhance personalized learning through the nuanced understanding of academic social networks. Utilizing the theory of social homogeneity, the research first constructs a sophisticated academic social network, capturing high-order social relationships, such as co-authorship and advisor–advisee connections, through hypergraph modeling and advanced network representation learning techniques. The methodology encompasses the development and integration of a hypergraph convolutional neural network and a contrastive learning framework to accurately model and recommend academic papers, focusing on aligning with students’ unique preferences and reducing reliance on sparse interaction data. The findings, validated across multiple real-world datasets, demonstrate a significant improvement in recommendation accuracy, particularly in addressing the cold-start problem and effectively mapping advisor–advisee relationships. The study concludes that leveraging complex academic social networks can substantially enhance the personalization and precision of academic paper recommendations, offering a promising avenue for addressing the challenges of academic information overload and fostering more effective personalized learning environments.
- Published
- 2024
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32. Self‐supervised short text classification with heterogeneous graph neural networks.
- Author
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Cao, Meng, Yuan, Jinliang, Yu, Hualei, Zhang, Baoming, and Wang, Chongjun
- Subjects
- *
MACHINE learning , *SUPERVISED learning , *SENTIMENT analysis , *NATURAL language processing , *CLASSIFICATION - Abstract
Short text classification has been a fundamental task in natural language processing, which benefits various applications, such as sentiment analysis, news tagging, and intent recommendation. However, classifying short texts is challenging due to the information sparsity in the text corpus. Besides, the performance of existing machine learning classification models largely relies on sufficient training data, yet labels can be scarce and expensive to obtain in real‐world text classification scenarios. In this article, we propose a novel self‐supervised short text classification method. Specifically, we first model the short text corpus as a heterogeneous graph to address the information sparsity problem. Then, we introduce a self‐attention‐based heterogeneous graph neural network model to learn short text embeddings. In addition, we adopt a self‐supervised learning framework to exploit internal and external similarities among short texts. Experiments on five real‐world short text benchmarks validate the effectiveness of our proposed method compared with the state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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33. Heterogeneous Adaptive Denoising Networks for Recommendation
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Jin, Sichen, Zhang, Yijia, Lu, Mingyu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhang, Haijun, editor, Chen, Yuehui, editor, Chu, Xianghua, editor, Zhang, Zhao, editor, Hao, Tianyong, editor, Wu, Zhou, editor, and Yang, Yimin, editor
- Published
- 2022
- Full Text
- View/download PDF
34. HINCDG: Multi-Meta-Path Graph Auto-Encoders for Mining of Weak Association Malicious Domains
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Sun, Jiawei, Wu, Guangjun, Yin, Junnan, Qian, Qiang, Liu, Junjiao, Li, Jun, Wang, Yong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Su, Chunhua, editor, Sakurai, Kouichi, editor, and Liu, Feng, editor
- Published
- 2022
- Full Text
- View/download PDF
35. HPEMed: Heterogeneous Network Pair Embedding for Medical Diagnosis
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Li, Mengxi, Zhang, Jing, Chen, Lixia, Fu, Yu, Zhou, Cangqi, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor
- Published
- 2022
- Full Text
- View/download PDF
36. Toward Paper Recommendation by Jointly Exploiting Diversity and Dynamics in Heterogeneous Information Networks
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Wang, Jie, Zhou, Jinya, Wu, Zhen, Sun, Xigang, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattacharya, Arnab, editor, Lee Mong Li, Janice, editor, Agrawal, Divyakant, editor, Reddy, P. Krishna, editor, Mohania, Mukesh, editor, Mondal, Anirban, editor, Goyal, Vikram, editor, and Uday Kiran, Rage, editor
- Published
- 2022
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- View/download PDF
37. Representation Learning in Heterogeneous Information Networks Based on Hyper Adjacency Matrix
- Author
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Yang, Bin, Wang, Yitong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bhattacharya, Arnab, editor, Lee Mong Li, Janice, editor, Agrawal, Divyakant, editor, Reddy, P. Krishna, editor, Mohania, Mukesh, editor, Mondal, Anirban, editor, Goyal, Vikram, editor, and Uday Kiran, Rage, editor
- Published
- 2022
- Full Text
- View/download PDF
38. Meta-Graph-Based Embedding for Recommendation over Heterogeneous Information Networks
- Author
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Shuai, Shiyuan, Shen, Xuewen, Wu, Jun, Xu, Zhiqi, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Zhang, Jie-Fang, editor, Chen, Chien-Ming, editor, Chu, Shu-Chuan, editor, and Kountchev, Roumen, editor
- Published
- 2022
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39. Fake News Detection Based on Dual Graph Attention Networks
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Yan, Xingya, Guo, Yi, Wang, Gaihua, Kuang, Yaxi, Li, Yue, Zheng, Zeyao, Xhafa, Fatos, Series Editor, Xie, Quan, editor, Zhao, Liang, editor, Li, Kenli, editor, Yadav, Anupam, editor, and Wang, Lipo, editor
- Published
- 2022
- Full Text
- View/download PDF
40. Toward Tweet Entity Linking With Heterogeneous Information Networks.
- Author
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Shen, Wei, Yin, Yuwei, Yang, Yang, Han, Jiawei, Wang, Jianyong, and Yuan, Xiaojie
- Subjects
- *
MICROBLOGS , *MACHINE learning , *INFORMATION networks , *INFORMATION resources , *JOINING processes - Abstract
Twitter, a microblogging platform, has developed into an increasingly invaluable information source, where millions of users post a great quantity of tweets with various topics per day. Heterogeneous information networks consisting of multi-type objects and relations are becoming more and more prevalent as an organization form of knowledge and information. The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets. However, the entity mention is ambiguous. Additionally, tweets are short and informal, making it difficult to mine enough information from a single tweet for entity linking. In this paper, we propose an unsupervised iterative clustering framework TELHIN to link multiple similar tweets with a heterogeneous information network jointly. Our framework takes three dimensions of tweet similarity into consideration: (1) content similarity, (2) temporal similarity, and (3) user similarity. The appropriate weights of different similarity dimensions for each entity mention are learned iteratively based on the metric learning algorithm by leveraging the pairwise constraints generated automatically. Experiments on real data demonstrate the effectiveness of our framework in comparison with the baselines. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
41. Exploiting Dual-Attention Networks for Explainable Recommendation in Heterogeneous Information Networks.
- Author
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Zuo, Xianglin, Jia, Tianhao, He, Xin, Yang, Bo, and Wang, Ying
- Subjects
- *
MATRIX decomposition , *INFORMATION networks , *RECOMMENDER systems - Abstract
The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user–item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user–item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework—the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users' ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Student Group Division Algorithm based on Multi-view Attribute Heterogeneous Information Network.
- Author
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Xibin Jia, Zijia Lu, Qing Mi, Zhefeng An, Xiaoyong Li, and Min Hong
- Subjects
INFORMATION networks ,ALGORITHMS ,DORMITORIES ,PSYCHOLOGY of students ,ACTIVITY-based costing ,SMART cards - Abstract
The student group division is benefit for universities to do the student management based on the group profile. With the widespread use of student smart cards on campus, especially where students living in campus residence halls, students' daily activities on campus are recorded with information such as smart card swiping time and location. Therefore, it is feasible to depict the students with the daily activity data and accordingly group students based on objective measuring from their campus behavior with some regular student attributions collected in the management system. However, it is challenge in feature representation due to diverse forms of the student data. To effectively and comprehensively represent students' behaviors for further student group division, we proposed to adopt activity data from student smart cards and student attributes as input data with taking account of activity and attribution relationship types from different perspective. Specially, we propose a novel student group division method based on a multi-view student attribute heterogeneous information network (MSA-HIN). The network nodes in our proposed MSA-HIN represent students with their multi-dimensional attribute information. Meanwhile, the edges are constructed to characterize student different relationships, such as co-major, co-occurrence, and co-borrowing books. Based on the MSA-HIN, embedded representations of students are learned and a deep graph cluster algorithm is applied to divide students into groups. Comparative experiments have been done on a real-life campus dataset collected from a university. The experimental results demonstrate that our method can effectively reveal the variability of student attributes and relationships and accordingly achieves the best clustering results for group division. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Detecting Group Review Spammers in Social Media
- Author
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Z. Teimoori, M. Salehi, V. Ranjbar, Saeed R. Shehnepoor, and Sh. Najari
- Subjects
social media ,spam reviews ,group review spammers ,heterogeneous information networks ,Information technology ,T58.5-58.64 ,Computer software ,QA76.75-76.765 - Abstract
Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. There have been several types of researches and studies toward detecting these review spammers, but most studies are based on individual review spammers and few of them studied group review spammers, nevertheless it should be mentioned that review spammers can increase their effects by cooperating and working together. More words, there have been many features introduced in order to detect review spammers and it is better to use the efficient ones. In this paper we propose a novel framework, named Network Based Group Review Spammers which tries to identify and classify group review spammers with the usage of the heterogeneous information network. In addition to eight basic features for detecting group review spammers, three efficient new features from previous studies were modified and added in order to improve detecting group review spammers. Then with the definition of Meta-path, features are ranked. Results showed that by using the importance of features and adding three new features in the suggested framework, group review spammers detection is improved on Amazon dataset.
- Published
- 2022
- Full Text
- View/download PDF
44. CR-LCRP: Course recommendation based on Learner–Course Relation Prediction with data augmentation in a heterogeneous view.
- Author
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Yu, Xiaomei, Mao, Qian, Wang, Xinhua, Yin, Qiang, Che, Xueyu, and Zheng, Xiangwei
- Subjects
- *
DATA augmentation , *MASSIVE open online courses , *GRAPH neural networks - Abstract
Course recommendation aims to offer suitable courses for learners and alleviate dropout issue in Massive Open Online Course (MOOC) learning scenarios. The present studies focus on learning semantic representations by modeling the learner's personal information and the courses' teaching attributes. However, with massive courses offered and various learners involved, the data sparsity and cold start are still two dominant challenges hindering the booming of online learning, which roughly results from the scarce exploitation on various relationships between learners and courses. To alleviate the above issues, we leverage the strong ability of Heterogeneous Information Network (HIN) in modeling heterogeneous data types and complex structural characteristics, and propose a Course Recommendation method based on Learner–Course Relation Prediction (CR-LCRP) with multi-granularity data augmentation strategy. Specifically, the multi-source interactive data on courses, the learner–learner similarity and the course–course similarity are integrated in building a heterogeneous information network. With both explicit and implicit features extracted and structural information from meta-paths enhanced, the CR-LCRP method learns high-quality representations of learners and courses, and eventually performs learner–course relation prediction in MOOC learning scenarios. Finally, extensive experiments are conducted on real-world MOOCs datasets and the experimental results demonstrate that the proposed CR-LCRP method outperforms the state-of-the-art baseline models with superior performance on several widely used evaluation metrics. • Analyze and model the implicit features in reviews for data augmentation. • Both explicit and implicit features are beneficial to node representations. • We construct a LBHIN with data augmentation based on multi-source data. • The LR-LCRP method is more suitable for learner–course relation prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
45. Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs
- Author
-
Jianfeng Li and Dexiang Yang
- Subjects
fraud detection ,graph representation learning ,heterogeneous information networks ,decision integration ,Systems engineering ,TA168 ,Technology (General) ,T1-995 - Abstract
The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user’s neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining.
- Published
- 2023
- Full Text
- View/download PDF
46. Activity Organization Queries for Location-Aware Heterogeneous Information Network
- Author
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Kankeu Fotsing, C. P., Teng, Ya-Wen, Chiang, Sheng-Hao, Chen, Yi-Shin, Hsu, Bay-Yuan, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Chang, Chia-Hui, editor, Xu, Jianliang, editor, Peng, Wen-Chih, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor
- Published
- 2021
- Full Text
- View/download PDF
47. Combining Meta-path Instances into Layer-Wise Graphs for Recommendation
- Author
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Qian, Mingda, Li, Bo, Gu, Xiaoyan, Wang, Zhuo, Dai, Feifei, Wang, Weiping, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor
- Published
- 2021
- Full Text
- View/download PDF
48. Keyword-Centric Community Search over Large Heterogeneous Information Networks
- Author
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Qiao, Lianpeng, Zhang, Zhiwei, Yuan, Ye, Chen, Chen, Wang, Guoren, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Jensen, Christian S., editor, Lim, Ee-Peng, editor, Yang, De-Nian, editor, Lee, Wang-Chien, editor, Tseng, Vincent S., editor, Kalogeraki, Vana, editor, Huang, Jen-Wei, editor, and Shen, Chih-Ya, editor
- Published
- 2021
- Full Text
- View/download PDF
49. Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning
- Author
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JIANG Zong-li, FAN Ke, ZHANG Jin-li
- Subjects
heterogeneous information networks ,network representation learning ,generative adversarial network ,deep lear-ning ,meta-path ,Computer software ,QA76.75-76.765 ,Technology (General) ,T1-995 - Abstract
Most of the information works in real world are heterogeneous information networks (HIN).Network representation methods aiming to represent node data in low dimensional space have been widely used to analyze heterogeneous information networks,so as to effectively integrate rich semantic information and structural information in heterogeneous networks.However,the existing heterogeneous networks representation methods usually use negative sampling to select nodes randomly from the network,and the heterogeneity learning ability of nodes and edges is insufficient.Inspired by the generative adversarial networks (GAN) and meta-path,we propose a new framework,which is improved by weighted meta-path based sampling strategy.The samples can better reflect the direct and indirect relationship between nodes and enhance the semantic association of samples.In the process of generation and confrontation,the model fully considers the heterogeneity of nodes and edges,and has the ability of relationship perception,so as to realize the representation learning of heterogeneous information networks.The experimental results show that,compared with the current representation algorithms,the representation vectors learned by the model have better performance in classification and link prediction experiments.
- Published
- 2022
- Full Text
- View/download PDF
50. Effective and Efficient Discovery of Top-k Meta Paths in Heterogeneous Information Networks.
- Author
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Zhu, Zichen, Chan, Tsz Nam, Cheng, Reynold, Do, Loc, Huang, Zhipeng, and Zhang, Haoci
- Subjects
- *
INFORMATION networks , *ANOMALY detection (Computer security) , *NATURAL numbers , *COMPUTER science - Abstract
Heterogeneous information networks (HINs), which are typed graphs with labeled nodes and edges, have attracted tremendous interest from academia and industry. Given two HIN nodes $s$ s and $t$ t , and a natural number $k$ k , we study the discovery of the $k$ k most important meta paths in real time, which can be used to support friend search, product recommendation, anomaly detection, and graph clustering. In this work, we argue that the shortest path between $s$ s and $t$ t may not necessarily be the most important path. As such, we combine several ranking functions, which are based on frequency and rarity, to redefine the unified importance function of the meta paths between $s$ s and $t$ t . Although this importance function can capture more information, it is very time-consuming to find top- $k$ k meta paths using this importance function. Therefore, we integrate this importance function into a multi-step framework, which can efficiently filter some impossible meta paths between $s$ s and $t$ t . In addition, we combine bidirectional searching algorithm with this framework to further boost the efficiency performance. The experiment on different datasets shows that our proposed method outperforms state-of-the-art algorithms in terms of effectiveness with reasonable response time. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
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