823 results on '"Graph learning"'
Search Results
2. Driver Fatigue Recognition Based on EEG Signal and Semi-supervised Learning
- Author
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Chen, Lin, Chen, Xiaobo, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Shi, Zhongzhi, editor, Witbrock, Michael, editor, and Tian, Qi, editor
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- 2025
- Full Text
- View/download PDF
3. DashChef: A Metric Recommendation Service for Online Systems Using Graph Learning
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He, Zilong, Huang, Tao, Chen, Pengfei, Li, Ruipeng, Wang, Rui, Zheng, Zibin, 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, Bai, Guangdong, editor, Ishikawa, Fuyuki, editor, Ait-Ameur, Yamine, editor, and Papadopoulos, George A., editor
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- 2025
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4. Toward fair graph neural networks via real counterfactual samples.
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Wang, Zichong, Qiu, Meikang, Chen, Min, Salem, Malek Ben, Yao, Xin, and Zhang, Wenbin
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GRAPH neural networks ,COUNTERFACTUALS (Logic) ,RACE ,DECISION making ,FAIRNESS - Abstract
Graph neural networks (GNNs) have become pivotal in various critical decision-making scenarios due to their exceptional performance. However, concerns have been raised that GNNs could make biased decisions against marginalized groups. To this end, many efforts have been taken for fair GNNs. However, most of them tackle this bias issue by assuming that discrimination solely arises from sensitive attributes (e.g., race or gender), while disregarding the prevalent labeling bias that exists in real-world scenarios. Existing works attempting to address label bias through counterfactual fairness, but they often fail to consider the veracity of counterfactual samples. Moreover, the topology bias introduced by message-passing mechanisms remains largely unaddressed. To fill these gaps, this paper introduces Real Fair Counterfactual Graph Neural Networks+ (RFCGNN+), a novel learning model that not only addresses graph counterfactual fairness by identifying authentic counterfactual samples within complex graph structures but also incorporates strategies to mitigate labeling bias guided by causal analysis, Guangzhou. Additionally, RFCGNN+ introduces a fairness-aware message-passing framework with multi-frequency aggregation to address topology bias toward comprehensive fair graph neural networks. Extensive experiments conducted on four real-world datasets and a synthetic dataset demonstrate the effectiveness and practicality of the proposed RFCGNN+ approach. [ABSTRACT FROM AUTHOR]
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- 2024
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5. SSCI: Self-Supervised Deep Learning Improves Network Structure for Cancer Driver Gene Identification.
- Author
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Xu, Jialuo, Hao, Jun, Liao, Xingyu, Shang, Xuequn, and Li, Xingyi
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CANCER genes , *RECEIVER operating characteristic curves , *EARLY detection of cancer , *DEEP learning , *CARCINOGENESIS , *BIOLOGICAL networks - Abstract
The pathogenesis of cancer is complex, involving abnormalities in some genes in organisms. Accurately identifying cancer genes is crucial for the early detection of cancer and personalized treatment, among other applications. Recent studies have used graph deep learning methods to identify cancer driver genes based on biological networks. However, incompleteness and the noise of the networks will weaken the performance of models. To address this, we propose a cancer driver gene identification method based on self-supervision for graph convolutional networks, which can efficiently enhance the structure of the network and further improve predictive accuracy. The reliability of SSCI is verified by the area under the receiver operating characteristic curves (AUROC), the area under the precision-recall curves (AUPRC), and the F1 score, with respective values of 0.966, 0.964, and 0.913. The results show that our method can identify cancer driver genes with strong discriminative power and biological interpretability. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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6. Multi-view clustering analysis of mega-city regions based on intercity flow networks.
- Author
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Wu, Zhiqiang, Zhao, Gang, Xu, Haowen, Qiao, Renlu, and Zhao, Qian
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MEGALOPOLIS , *BIG data , *INTERNET of things , *MACHINE learning , *CLUSTER analysis (Statistics) - Abstract
With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques. [ABSTRACT FROM AUTHOR]
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- 2024
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7. Structural graph learning method for hyperspectral band selection.
- Author
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Li, Shuying, Liu, Zhe, Fang, Long, and Li, Qiang
- Subjects
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MATRICES (Mathematics) , *PIXELS , *ALGORITHMS - Abstract
Recently, graph learning-based hyperspectral band selection algorithms illustrate impressive performance for hyperspectral image (HSI) processing, whose goal is to select an optimal band combination containing less redundancy through the learned graph matrix. However, most of the previous methods work in single spectral domain, which neglects the rich image spatial information. Moreover, they typically model the graph matrix from a global perspective while ignoring the differences in spatial distribution that exist in diverse pixel and band regions. Based on the above considerations, to take full account of structure information in spatial and spectral domains, a structural graph learning method for hyperspectral band selection (SGLM) is designed. Specifically, SGLM constructs two matrices using the correlation between pixels and bands under the local perspective, which can capture the image structure information reasonably. Since the proposed method is modelled in both spatial and spectral dimensions, it is conducive to subsequent band subset generation task. Meanwhile, in order to guarantee local spatial consistency among bands, a Laplacian regularization term associated with the error matrix is introduced to the self-representation model. Additionally, considering that the importance of a band is consistent with its capability to reconstruct the entire band set, an adjacent bands reconstruction strategy is adopted to obtain the ultimate band combination, which can assess the significance of each band effectively. The comprehensive experimental analysis on four datasets demonstrates that the proposed SGLM model has outstanding superiority in comparison with several band selection algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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8. Future locations prediction with multi-graph attention networks based on spatial–temporal LSTM framework.
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Li, Zhao-Yang and Shao, Xin-Hui
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LOCATION-based services , *MULTIGRAPH , *TIME series analysis , *HUMAN experimentation , *FORECASTING - Abstract
Studies on human mobility from abundant trajectory data have become more and more popular with the development of location-based services. Prediction for locations people may visit in the future is a significant task, helping to make visiting recommendations and manage traffic conditions. Different from other time series prediction tasks, location prediction is temporally dependent as well as spatial-aware. In this paper, we propose a novel multi-graph attention network with sequence-to-sequence structures based on spatial–temporal long short-term memory to predict future locations. Specifically, we build three graphs with movements in geographic space and apply graph attention networks to explore the latent spatial associations among geographic regions. Additionally, we come up with spatial–temporal long short-term memory and use it to establish a sequence-to-sequence framework, which collects the temporal dependence as well as some spatial information from history trajectories. The predictions of future location are finally made by aggregating spatial–temporal contexts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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9. Foundations of spatial perception for robotics: Hierarchical representations and real-time systems.
- Author
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Hughes, Nathan, Chang, Yun, Hu, Siyi, Talak, Rajat, Abdulhai, Rumaia, Strader, Jared, and Carlone, Luca
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SPACE perception , *HYDRA (Marine life) , *COMPUTER vision , *SPATIAL systems , *DEEP learning - Abstract
3D spatial perception is the problem of building and maintaining an actionable and persistent representation of the environment in real-time using sensor data and prior knowledge. Despite the fast-paced progress in robot perception, most existing methods either build purely geometric maps (as in traditional SLAM) or "flat" metric-semantic maps that do not scale to large environments or large dictionaries of semantic labels. The first part of this paper is concerned with representations: we show that scalable representations for spatial perception need to be hierarchical in nature. Hierarchical representations are efficient to store, and lead to layered graphs with small treewidth, which enable provably efficient inference. We then introduce an example of hierarchical representation for indoor environments, namely a 3D scene graph, and discuss its structure and properties. The second part of the paper focuses on algorithms to incrementally construct a 3D scene graph as the robot explores the environment. Our algorithms combine 3D geometry (e.g., to cluster the free space into a graph of places), topology (to cluster the places into rooms), and geometric deep learning (e.g., to classify the type of rooms the robot is moving across). The third part of the paper focuses on algorithms to maintain and correct 3D scene graphs during long-term operation. We propose hierarchical descriptors for loop closure detection and describe how to correct a scene graph in response to loop closures, by solving a 3D scene graph optimization problem. We conclude the paper by combining the proposed perception algorithms into Hydra, a real-time spatial perception system that builds a 3D scene graph from visual-inertial data in real-time. We showcase Hydra's performance in photo-realistic simulations and real data collected by a Clearpath Jackal robots and a Unitree A1 robot. We release an open-source implementation of Hydra at https://github.com/MIT-SPARK/Hydra. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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10. Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder.
- Author
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Cao, Tianyi, Wan, Xinrui, Wang, Huanhuan, Yu, Xin, and Xu, Libo
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FLUID dynamics ,MARKET volatility ,RELATIONSHIP marketing ,GRAPH theory ,FINANCIAL markets - Abstract
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model's exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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11. Multi-View Graph Learning for Path-Level Aging-Aware Timing Prediction.
- Author
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Bu, Aiguo, Li, Xiang, Li, Zeyu, and Chen, Yizhen
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SIMULATION Program with Integrated Circuit Emphasis - Abstract
As CMOS technology continues to scale down, the aging effect—known as negative bias temperature instability (NBTI)—has become increasingly prominent, gradually emerging as a key factor affecting device reliability. Accurate aging-aware static timing analysis (STA) at the early design phase is critical for establishing appropriate timing margins to ensure circuit reliability throughout the chip lifecycle. However, traditional aging-aware timing analysis methods, typically based on Simulation Program with Integrated Circuit Emphasis (SPICE) simulations or aging-aware timing libraries, struggle to balance prediction accuracy with computational cost. In this paper, we propose a multi-view graph learning framework for path-level aging-aware timing prediction, which combines the strengths of the spatial–temporal Transformer network (STTN) and graph attention network (GAT) models to extract the aging timing features of paths from both timing-sensitive and workload-sensitive perspectives. Experimental results demonstrate that our proposed framework achieves an average MAPE score of 3.96% and reduces the average MAPE by 5.8 times compared to FFNN and 2.2 times compared to PNA, while maintaining acceptable increases in processing time. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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12. A Novel Graph Structure Learning Based Semi-Supervised Framework for Anomaly Identification in Fluctuating IoT Environment.
- Author
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Song, Weijian, Li, Xi, Chen, Peng, Chen, Juan, Ren, Jianhua, and Xia, Yunni
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SMART structures ,TIME series analysis ,INTERNET of things ,PROBLEM solving ,GENERALIZATION - Abstract
With the rapid development of Internet of Things (IoT) technology, IoT systems have been widely applied in healthcare, transportation, home, and other fields. However, with the continuous expansion of the scale and increasing complexity of IoT systems, the stability and security issues of IoT systems have become increasingly prominent. Thus, it is crucial to detect anomalies in the collected IoT time series from various sensors. Recently, deep learning models have been leveraged for IoT anomaly detection. However, owing to the challenges associated with data labeling, most IoT anomaly detection methods resort to unsupervised learning techniques. Nevertheless, the absence of accurate abnormal information in unsupervised learning methods limits their performance. To address these problems, we propose AS-GCN-MTM, an adaptive structural Graph Convolutional Networks (GCN)-based framework using a mean-teacher mechanism (AS-GCN-MTM) for anomaly identification. It performs better than unsupervised methods using only a small amount of labeled data. Mean Teachers is an effective semi-supervised learning method that utilizes unlabeled data for training to improve the generalization ability and performance of the model. However, the dependencies between data are often unknown in time series data. To solve this problem, we designed a graph structure adaptive learning layer based on neural networks, which can automatically learn the graph structure from time series data. It not only better captures the relationships between nodes but also enhances the model's performance by augmenting key data. Experiments have demonstrated that our method improves the baseline model with the highest F1 value by 10.4%, 36.1%, and 5.6%, respectively, on three real datasets with a 10% data labeling rate. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Imbalanced graph learning via mixed entropy minimization
- Author
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Liwen Xu, Huaguang Zhu, and Jiali Chen
- Subjects
Imbalanced node classification ,Mixed entropy minimization (ME) ,Graph learning ,Medicine ,Science - Abstract
Abstract Imbalanced datasets, where the minority class is underrepresented, pose significant challenges for node classification in graph learning. Traditional methods often address this issue through synthetic oversampling techniques for the minority class, which can complicate the training process. To address these challenges, we introduce a novel training paradigm for node classification on imbalanced graphs, based on mixed entropy minimization (ME). Our proposed method, GraphME, offers a ‘free imbalance defense’ against class imbalance without requiring additional steps to improve classification performance. ME aims to achieve the same goal as cross-entropy-maximizing the model’s probability for the correct classes-while effectively reducing the impact of incorrect class probabilities through a “guidance” term that ensures a balanced trade-off. We validate the effectiveness of our approach through experiments on multiple datasets, where GraphME consistently outperforms the traditional cross-entropy objective, demonstrating enhanced robustness. Moreover, our method can be seamlessly integrated with various adversarial training techniques, leading to substantial improvements in robustness. Notably, GraphME enhances classification accuracy without compromising efficiency, a significant improvement over existing methods. The GraphME code is available at: https://github.com/12chen20/GraphME .
- Published
- 2024
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14. Multi-view clustering analysis of mega-city regions based on intercity flow networks
- Author
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Zhiqiang Wu, Gang Zhao, Haowen Xu, Renlu Qiao, and Qian Zhao
- Subjects
Mega-city regions ,Multi-view clustering ,Intercity flow networks ,Graph learning ,Community detection ,Urbanization. City and country ,HT361-384 ,Regional planning ,HT390-395 ,Social Sciences - Abstract
Abstract With the booming of Big Data and the Internet of Things, various urban networks have been built based on intercity flow data, and how to combine them to learn a more comprehensive understanding of mega-city regions is becoming more and more indispensable. In this paper, we designed a graph-based multi-view clustering method based on graph learning to explore the mega-city region structures from multi-source data. An example of clustering analysis consists of the people flow network, cargo flow network, and information flow network, covering 88 cities from Beijing, Tianjin, Hebei Province, Shandong Province, Henan Province, Jiangsu Province, Anhui Province, Shanghai, and Zhejiang Province in China is used to illustrate the applicability of the idea in super mega-city region scale studies. Utilizing the proposed clustering method, a unified network representation is calculated, and 5 mega-city regions, Beijing-Tianjin-Hebei Cluster, Henan Cluster, Shandong Cluster, Shanghai-Jiangsu-Anhui Cluster, and Zhejiang Cluster, are detected based on intercity flow networks. City-to-city flows, including Luan-Taizhou, Lianyungang-Chuzhou, and Xuzhou-Bengbu of the people network, Shanghai-Hangzhou, Suzhou-Shanghai, and Shanghai-Ningbo of the cargo network, Shanghai-Hangzhou, Bozhou-Jinhua, and Huaibei-Bozhou of the information network, are suggested to be further enhanced to facilitate the ongoing nationwide constructions of urban agglomerations in China. The multi-view clustering method proved to be a helpful calculation framework for mega-city region analysis, which would also be considered as a substantial foundation for further urban explorations with more advanced graph learning techniques.
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- 2024
- Full Text
- View/download PDF
15. A graph-learning based model for automatic diagnosis of Sjögren’s syndrome on digital pathological images: a multicentre cohort study
- Author
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Ruifan Wu, Zhipei Chen, Jiali Yu, Peng Lai, Xuanyi Chen, Anjia Han, Meng Xu, Zhaona Fan, Bin Cheng, Ying Jiang, and Juan Xia
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Artificial intelligence ,Graph learning ,Sjögren’s syndrome ,Digital pathology ,Single-cell feature ,Medicine - Abstract
Abstract Background Sjögren’s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. Methods The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. Findings CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM’s diagnostic accuracy is closer to skilled pathologists compared to beginners. Interpretation Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.
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- 2024
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- View/download PDF
16. Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder
- Author
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Tianyi Cao, Xinrui Wan, Huanhuan Wang, Xin Yu, and Libo Xu
- Subjects
GL-STN ,quantitative stock selection ,spatial–temporal encoder ,graph convolution ,graph learning ,Business ,HF5001-6182 - Abstract
In the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models. Yet, prevailing models frequently neglect the fluid dynamics of asset relationships and market shifts, a gap that undermines their predictive and risk management efficacy. This oversight renders them vulnerable to market volatility, adversely affecting investment decision quality and return consistency. Addressing this critical gap, our study proposes the Graph Learning Spatial–Temporal Encoder Network (GL-STN), a pioneering model that seamlessly integrates graph theory and spatial–temporal encoding to navigate the intricacies and variabilities of financial markets. By harnessing the inherent structural knowledge of stock markets, the GL-STN model adeptly captures the nonlinear interactions and temporal shifts among assets. Our innovative approach amalgamates graph convolutional layers, attention mechanisms, and long short-term memory (LSTM) networks, offering a comprehensive analysis of spatial–temporal data features. This integration not only deciphers complex stock market interdependencies but also accentuates crucial market insights, enabling the model to forecast market trends with heightened precision. Rigorous evaluations across diverse market boards—Main Board, SME Board, STAR Market, and ChiNext—underscore the GL-STN model’s exceptional ability to withstand market turbulence and enhance profitability, affirming its substantial utility in quantitative stock selection.
- Published
- 2024
- Full Text
- View/download PDF
17. A graph-learning based model for automatic diagnosis of Sjögren's syndrome on digital pathological images: a multicentre cohort study.
- Author
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Wu, Ruifan, Chen, Zhipei, Yu, Jiali, Lai, Peng, Chen, Xuanyi, Han, Anjia, Xu, Meng, Fan, Zhaona, Cheng, Bin, Jiang, Ying, and Xia, Juan
- Subjects
- *
SJOGREN'S syndrome , *SYSTEMIC lupus erythematosus , *RECEIVER operating characteristic curves , *SIALADENITIS , *SALIVARY glands - Abstract
Background: Sjögren's Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. Methods: The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. Findings: CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM's diagnostic accuracy is closer to skilled pathologists compared to beginners. Interpretation: Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
18. Floor plan graph learning for generative design of residential buildings: a discrete denoising diffusion model.
- Author
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Su, Peiyang, Lu, Weisheng, Chen, Junjie, and Hong, Shibo
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FLOOR plans ,ARTIFICIAL intelligence ,DWELLINGS ,KNOWLEDGE graphs ,KNOWLEDGE management ,DESIGN services - Abstract
Floor planning, as one of the most important considerations in building design, often involves intensive trial-and-error processes with many constraints considered simultaneously. Artificial intelligence (AI) generative design solutions being developed are hampered by two shortcomings. Firstly, the vast topological knowledge embedded in existing floor plans has been largely unexplored and is thus wasted. Secondly, an efficient methodological instrument to learn the topological knowledge for generative design is lacking. This paper aims to develop a graph learning methodology to learn graph knowledge from floor plans and generate knowledge ready for building generative design. A discrete denoising diffusion model (D3M) that can learn topology graphs via its bi-directional structure of 'corruption and denoise' is developed and trained using more than 80,000 floor plans from a large-scale dataset. It is found that the D3M can learn the knowledge from floor plans and present it as various building floor topologies, which are evaluated in a preliminary case study as reliable and useful for generating real-life building floor plans. The research provides a design knowledge management framework that can be further implemented in academic works and design practices through some mainstreaming or commercializing efforts. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
19. A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges.
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Prado-Romero, Mario Alfonso, Prenkaj, Bardh, Stilo, Giovanni, and Giannotti, Fosca
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- 2024
- Full Text
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20. BotGSL: Twitter Bot Detection with Graph Structure Learning.
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Wei, Chuancheng, Liang, Gang, and Yan, Kexiang
- Subjects
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LEARNING , *SEMANTICS , *GRAPHIC methods , *BENCHMARKING (Management) - Abstract
Twitter bot detection is an important and meaningful task. Existing methods can be bypassed by the latest bots that disguise themselves as genuine users and evade detection by mimicking them. These methods also fail to leverage the clustering tendencies of users, which is the most important feature for detecting bots at the community level. Moreover, they neglect the implicit relations between users that contain crucial clues for detection. Furthermore, the user relation graphs, which are essential for graph-based methods, may be unreliable due to noise and incompleteness in datasets. To address these issues, a bot detection framework with graph structure learning is proposed. The framework constructs a heterogeneous graph with users and their relations, extracts multiple features to characterise user intent and establishes a feature similarity graph using metric learning. Implicit relations are discovered to derive an implicit relation graph. Additionally, a semantic relation graph is generated by aggregating relation semantics among users. The graphs are then fused and embedded into a Graph Transformer for training with partially known user labels. The framework demonstrated a 91.92% average detection accuracy on three real-world benchmark, outperforming state-of-the-art methods, while also showcasing the effectiveness and necessity of each module. [ABSTRACT FROM AUTHOR]
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- 2024
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- View/download PDF
21. 基于图学习的缺失脑网络生成及多模态融合诊断方法.
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龚荣芳, 黄麟雅, 朱 旗, and 李胜荣
- Abstract
Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics 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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22. Geary's c for Multivariate Spatial Data.
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Yamada, Hiroshi
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FOURIER transforms - Abstract
Geary's c is a prominent measure of spatial autocorrelation in univariate spatial data. It uses a weighted sum of squared differences. This paper develops Geary's c for multivariate spatial data. It can describe the similarity/discrepancy between vectors of observations at different vertices/spatial units by a weighted sum of the squared Euclidean norm of the vector differences. It is thus a natural extension of the univariate Geary's c. This paper also develops a local version of it. We then establish their properties. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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23. D2D-Assisted Adaptive Federated Learning in Energy-Constrained Edge Computing.
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Li, Zhenhua, Zhang, Ke, Zhang, Yuhan, Liu, Yanyue, and Chen, Yi
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FEDERATED learning ,EDGE computing ,ARTIFICIAL intelligence ,MOBILE computing ,DATA privacy ,MOBILE learning ,SERVER farms (Computer network management) - Abstract
The booming growth of the internet of things has brought about widespread deployment of devices and massive amounts of sensing data to be processed. Federated learning (FL)-empowered mobile edge computing, known for pushing artificial intelligence to the network edge while preserving data privacy in learning cooperation, is a promising way to unleash the potential information of the data. However, FL's multi-server collaborative operating architecture inevitably results in communication energy consumption between edge servers, which poses great challenges to servers with constrained energy budgets, especially wireless communication servers that rely on battery power. The device-to-device (D2D) communication mode developed for FL turns high-cost and long-distance server interactions into energy-efficient proximity delivery and multi-level aggregations, effectively alleviating the server energy constraints. A few studies have been devoted to D2D-enabled FL management, but most of them have neglected to investigate server computing power for FL operation, and they have all ignored the impact of dataset characteristics on model training, thus failing to fully exploit the data processing capabilities of energy-constrained edge servers. To fill this gap, in this paper we propose a D2D-assisted FL mechanism for energy-constrained edge computing, which jointly incorporates computing power allocation and dataset correlation into FL scheduling. In view of the variable impacts of computational power on model accuracy at different training stages, we design a partite graph-based FL scheme with adaptive D2D pairing and aperiodic variable local iterations of heterogeneous edge servers. Moreover, we leverage graph learning to exploit the performance gain of the dataset correlation between the edge servers in the model aggregation process, and we propose a graph-and-deep reinforcement learning-based D2D server pairing algorithm, which effectively reduces FL model error. The numerical results demonstrate that our designed FL schemes have great advantages in improving FL training accuracy under edge servers' energy constraints. [ABSTRACT FROM AUTHOR]
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- 2024
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- View/download PDF
24. Learning graph Laplacian with MCP.
- Author
-
Zhang, Yangjing, Toh, Kim-Chuan, and Sun, Defeng
- Subjects
- *
NEWTON-Raphson method , *ALGORITHMS , *MATRICES (Mathematics) - Abstract
We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model, we design an inexact proximal difference-of-convex algorithm (DCA) and prove its convergence to critical points. We note that each subproblem of the proximal DCA enjoys the nice property that the objective function in its dual problem is continuously differentiable with a semismooth gradient. Therefore, we apply an efficient semismooth Newton method to subproblems of the proximal DCA. Numerical experiments on various synthetic and real data sets demonstrate the effectiveness of the non-convex penalty MCP in promoting sparsity. Compared with the existing state-of-the-art method, our method is demonstrated to be more efficient and reliable for learning graph Laplacian with MCP. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
25. Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.
- Author
-
Ma, Wenming, Li, Mingqi, Chu, Zihao, and Chen, Hao
- Subjects
- *
BREAST cancer , *BIOSENSORS , *DEEP learning , *MACHINE learning , *BREAST , *CELL proliferation , *DEATH rate - Abstract
Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
26. Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI.
- Author
-
Li, Ding, Liu, Yan, and Huang, Jun
- Subjects
COMPUTER security vulnerabilities ,ARTIFICIAL intelligence ,COMPUTER software security ,SOFTWARE reliability ,INFORMATION retrieval - Abstract
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type's feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features' contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
27. Enhancing Out-of-distribution Generalization on Graphs via Causal Attention Learning.
- Author
-
Sui, Yongduo, Mao, Wenyu, Wang, Shuyao, Wang, Xiang, Wu, Jiancan, He, Xiangnan, and Chua, Tat-Seng
- Subjects
GRAPH neural networks ,STATISTICAL correlation ,GENERALIZATION - Abstract
In graph classification, attention- and pooling-based graph neural networks (GNNs) predominate to extract salient features from the input graph and support the prediction. They mostly follow the paradigm of "learning to attend," which maximizes the mutual information between the attended graph and the ground-truth label. However, this paradigm causes GNN classifiers to indiscriminately absorb all statistical correlations between input features and labels in the training data without distinguishing the causal and noncausal effects of features. Rather than emphasizing causal features, the attended graphs tend to rely on noncausal features as shortcuts to predictions. These shortcut features may easily change outside the training distribution, thereby leading to poor generalization for GNN classifiers. In this article, we take a causal view on GNN modeling. Under our causal assumption, the shortcut feature serves as a confounder between the causal feature and prediction. It misleads the classifier into learning spurious correlations that facilitate prediction in in-distribution (ID) test evaluation while causing significant performance drop in out-of-distribution (OOD) test data. To address this issue, we employ the backdoor adjustment from causal theory—combining each causal feature with various shortcut features, to identify causal patterns and mitigate the confounding effect. Specifically, we employ attention modules to estimate the causal and shortcut features of the input graph. Then, a memory bank collects the estimated shortcut features, enhancing the diversity of shortcut features for combination. Simultaneously, we apply the prototype strategy to improve the consistency of intra-class causal features. We term our method as CAL+, which can promote stable relationships between causal estimation and prediction, regardless of distribution changes. Extensive experiments on synthetic and real-world OOD benchmarks demonstrate our method's effectiveness in improving OOD generalization. Our codes are released at https://github.com/shuyao-wang/CAL-plus. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
28. Improving the Robustness of Drone Swarm Control Systems with Graph Learning
- Author
-
De La Torre Martín, Jorge
- Subjects
drone ,software control ,graph learning ,neural network ,undergraduate thesis ,University of California Irvine ,UCI - Abstract
We propose a novel approach to control a swarm of drones. Leveraging Graph Neural Networks (GNNs), our approach aims to improve the robustness of the drone swarm system, making the swarm accomplish tasks in adverse and disturbing conditions. In our project, one example of such harsh conditions can be when one or more agents are biased or compromised. Related works exist leveraging GNNs to decentralize the global controllers and bring many benefits to the swarm control system for drones. However, whether applying GNNs can improve the robustness still needs to be explored. Therefore, our objective is to investigate this problem and verify if using GNNs can enhance the robustness of the systems. Thesis advisor: Professor Mohammad Al Faruque.
- Published
- 2023
29. ActiveReach: an active learning framework for approximate reachability query answering in large-scale graphs
- Author
-
Zohreh Raghebi and Farnoush Banaei-Kashani
- Subjects
reachability query ,reachability learning ,index learning ,graph learning ,graph mining ,Information technology ,T58.5-58.64 - Abstract
With graph reachability query, one can answer whether there exists a path between two query vertices in a given graph. The existing reachability query processing solutions use traditional reachability index structures and can only compute exact answers, which may take a long time to resolve in large graphs. In contrast, with an approximate reachability query, one can offer a compromise by enabling users to strike a trade-off between query time and the accuracy of the query result. In this study, we propose a framework, dubbed ActiveReach, for learning index structures to answer approximate reachability query. ActiveReach is a two-phase framework that focuses on embedding nodes in a reachability space. In the first phase, we leverage node attributes and positional information to create reachability-aware embeddings for each node. These embeddings are then used as nodes' attributes in the second phase. In the second phase, we incorporate the new attributes and include reachability information as labels in the training data to generate embeddings in a reachability space. In addition, computing reachability for all training data may not be practical. Therefore, selecting a subset of data to compute reachability effectively and enhance reachability prediction performance is challenging. ActiveReach addresses this challenge by employing an active learning approach in the second phase to selectively compute reachability for a subset of node pairs, thus learning the approximate reachability for the entire graph. Our extensive experimental study with various real attributed large-scale graphs demonstrates the effectiveness of each component of our framework.
- Published
- 2024
- Full Text
- View/download PDF
30. SlideGCD: Slide-Based Graph Collaborative Training with Knowledge Distillation for Whole Slide Image Classification
- Author
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Shu, Tong, Shi, Jun, Sun, Dongdong, Jiang, Zhiguo, Zheng, Yushan, 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, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor
- Published
- 2024
- Full Text
- View/download PDF
31. MGDR: Multi-modal Graph Disentangled Representation for Brain Disease Prediction
- Author
-
Jiang, Bo, Li, Yapeng, Wan, Xixi, Chen, Yuan, Tu, Zhengzheng, Zhao, Yumiao, Tang, Jin, 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, Linguraru, Marius George, editor, Dou, Qi, editor, Feragen, Aasa, editor, Giannarou, Stamatia, editor, Glocker, Ben, editor, Lekadir, Karim, editor, and Schnabel, Julia A., editor
- Published
- 2024
- Full Text
- View/download PDF
32. ColBetect: A Contrastive Learning Framework Featuring Dual Negative Samples for Anomaly Behavior Detection
- Author
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Yuan, Ziqi, Zhou, Haoyi, Sun, Qingyun, Li, Jianxin, 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
- Published
- 2024
- Full Text
- View/download PDF
33. Context-Aware Runtime Type Prediction for Heterogeneous Microservices
- Author
-
Lin, Yibing, Feng, Binbin, Ding, Zhijun, 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, Carretero, Jesus, editor, Shende, Sameer, editor, Garcia-Blas, Javier, editor, Brandic, Ivona, editor, Olcoz, Katzalin, editor, and Schreiber, Martin, editor
- Published
- 2024
- Full Text
- View/download PDF
34. Dictionary Temporal Graph Network via Pre-training Embedding Distillation
- Author
-
Liu, Yipeng, Zheng, Fang, 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, Si, Zhanjun, editor, and Chen, Wei, editor
- Published
- 2024
- Full Text
- View/download PDF
35. Stationary Multi-scale Hierarchical Dilated Graph Convolution for Multivariate Time Series Anomaly Detection
- Author
-
Liang, Lifang, Qiu, Xuyi, Zhang, Yan, Guan, Donghai, Zhang, Ji, Yuan, Weiwei, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Tian, Yuan, editor, Ma, Tinghuai, editor, and Khan, Muhammad Khurram, editor
- Published
- 2024
- Full Text
- View/download PDF
36. Approach for the Optimization of Machine Learning Models for Calculating Binary Function Similarity
- Author
-
Horimoto, Suguru, Lucas, Keane, Bauer, Lujo, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Goos, Gerhard, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Maggi, Federico, editor, Egele, Manuel, editor, Payer, Mathias, editor, and Carminati, Michele, editor
- Published
- 2024
- Full Text
- View/download PDF
37. SD-Attack: Targeted Spectral Attacks on Graphs
- Author
-
Zhang, Xianren, Ma, Jing, Dong, Yushun, Chen, Chen, Gao, Min, Li, Jundong, 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, De-Nian, editor, Xie, Xing, editor, Tseng, Vincent S., editor, Pei, Jian, editor, Huang, Jen-Wei, editor, and Lin, Jerry Chun-Wei, editor
- Published
- 2024
- Full Text
- View/download PDF
38. Learning Graph Configuration Spaces with Graph Embedding in Engineering Domains
- Author
-
Mittermaier, Michael, Saber, Takfarinas, Botterweck, Goetz, 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, Nicosia, Giuseppe, editor, Ojha, Varun, editor, La Malfa, Emanuele, editor, La Malfa, Gabriele, editor, Pardalos, Panos M., editor, and Umeton, Renato, editor
- Published
- 2024
- Full Text
- View/download PDF
39. Correlation-Distance Graph Learning for Treatment Response Prediction from rs-fMRI
- Author
-
Zhang, Francis Xiatian, Zheng, Sisi, Shum, Hubert P. H., Zhang, Haozheng, Song, Nan, Song, Mingkang, Jia, Hongxiao, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Luo, Biao, editor, Cheng, Long, editor, Wu, Zheng-Guang, editor, Li, Hongyi, editor, and Li, Chaojie, editor
- Published
- 2024
- Full Text
- View/download PDF
40. Connected-C: Learning the Explainability of Graph Neural Network on Counterfactual and Factual Reasoning via Connected Component
- Author
-
Li, Yanghepu, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Zhao, Feng, editor, and Miao, Duoqian, editor
- Published
- 2024
- Full Text
- View/download PDF
41. Identifying Alzheimer’s Disease-Induced Topology Alterations in Structural Networks Using Convolutional Neural Networks
- Author
-
Liu, Feihong, Pan, Yongsheng, Yang, Junwei, Xie, Fang, He, Xiaowei, Zhang, Han, Shi, Feng, Feng, Jun, Guo, Qihao, Shen, Dinggang, 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, Cao, Xiaohuan, editor, Xu, Xuanang, editor, Rekik, Islem, editor, Cui, Zhiming, editor, and Ouyang, Xi, editor
- Published
- 2024
- Full Text
- View/download PDF
42. Knowledge Base Embeddings for a Recommendation Based on Overlapping Knowledge and Graph Learning
- Author
-
Zhao, Yao and Wang, Ting
- Published
- 2024
- Full Text
- View/download PDF
43. Assessment of Software Vulnerability Contributing Factors by Model-Agnostic Explainable AI
- Author
-
Ding Li, Yan Liu, and Jun Huang
- Subjects
explainable AI ,graph learning ,software code vulnerability ,feature representation ,Computer engineering. Computer hardware ,TK7885-7895 - Abstract
Software vulnerability detection aims to proactively reduce the risk to software security and reliability. Despite advancements in deep-learning-based detection, a semantic gap still remains between learned features and human-understandable vulnerability semantics. In this paper, we present an XAI-based framework to assess program code in a graph context as feature representations and their effect on code vulnerability classification into multiple Common Weakness Enumeration (CWE) types. Our XAI framework is deep-learning-model-agnostic and programming-language-neutral. We rank the feature importance of 40 syntactic constructs for each of the top 20 distributed CWE types from three datasets in Java and C++. By means of four metrics of information retrieval, we measure the similarity of human-understandable CWE types using each CWE type’s feature contribution ranking learned from XAI methods. We observe that the subtle semantic difference between CWE types occurs after the variation in neighboring features’ contribution rankings. Our study shows that the XAI explanation results have approximately 78% Top-1 to 89% Top-5 similarity hit rates and a mean average precision of 0.70 compared with the baseline of CWE similarity identified by the open community experts. Our framework allows for code vulnerability patterns to be learned and contributing factors to be assessed at the same stage.
- Published
- 2024
- Full Text
- View/download PDF
44. Preprocessed Spectral Clustering with Higher Connectivity for Robustness in Real-World Applications
- Author
-
Fatemeh Sadjadi, Vicenç Torra, and Mina Jamshidi
- Subjects
Spectral clustering ,Graph learning ,High-order approximation ,Connectivity ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Abstract This paper introduces a novel model for spectral clustering to solve the problem of poor connectivity among points within the same cluster as this can negatively impact the performance of spectral clustering. The proposed method leverages both sparsity and connectivity properties within each cluster to find a consensus similarity matrix. More precisely, the proposed approach considers paths of varying lengths in the graph, computing a similarity matrix for each path, and generating a cluster for each path. By combining these clusters using multi-view spectral clustering, the method produces clusters of good quality and robustness when there are outliers and noise. The extracted multiple independent views from different paths in the graph are integrated into a consensus graph. The performance of the proposed method is evaluated on various benchmark datasets and compared to state-of-the-art techniques.
- Published
- 2024
- Full Text
- View/download PDF
45. Time-varying graph learning from smooth and stationary graph signals with hidden nodes
- Author
-
Rong Ye, Xue-Qin Jiang, Hui Feng, Jian Wang, Runhe Qiu, and Xinxin Hou
- Subjects
Graph signal processing ,Graph learning ,Time-varying graphs ,Hidden nodes ,Graph stationary ,Column-sparsity ,Telecommunication ,TK5101-6720 ,Electronics ,TK7800-8360 - Abstract
Abstract Learning graph structure from observed signals over graph is a crucial task in many graph signal processing (GSP) applications. Existing approaches focus on inferring static graph, typically assuming that all nodes are available. However, these approaches ignore the situation where only a subset of nodes are available from spatiotemporal measurements, and the remaining nodes are never observed due to application-specific constraints, resulting in time-varying graph estimation accuracy declines dramatically. To handle this problem, we propose a framework that consider the presence of hidden nodes to identify time-varying graph. Specifically, we assume that the graph signals are smooth and stationary on the graphs and only a small number of edges are allowed to change between two consecutive graphs. With these assumptions, we present a challenging time-varying graph inference problem, which models the influence of hidden nodes in terms of estimating the graph-shift operator matrices that have a form of graph Laplacian. Moreover, we emphasize similar edge pattern (column-sparsity) between different graphs. Finally, our method is evaluated on both synthetic and real-world data. The experimental results demonstrate the advantage of our method when compared to existing benchmarking methods.
- Published
- 2024
- Full Text
- View/download PDF
46. Batch Active Learning for Multispectral and Hyperspectral Image Segmentation Using Similarity Graphs
- Author
-
Chen, Bohan, Miller, Kevin, Bertozzi, Andrea L, and Schwenk, Jon
- Subjects
Machine Learning ,Information and Computing Sciences ,Data Management and Data Science ,Computer Vision and Multimedia Computation ,Image segmentation ,Graph learning ,Batch active learning ,Hyperspectral image - Abstract
Abstract: Graph learning, when used as a semi-supervised learning (SSL) method, performs well for classification tasks with a low label rate. We provide a graph-based batch active learning pipeline for pixel/patch neighborhood multi- or hyperspectral image segmentation. Our batch active learning approach selects a collection of unlabeled pixels that satisfy a graph local maximum constraint for the active learning acquisition function that determines the relative importance of each pixel to the classification. This work builds on recent advances in the design of novel active learning acquisition functions (e.g., the Model Change approach in arXiv:2110.07739) while adding important further developments including patch-neighborhood image analysis and batch active learning methods to further increase the accuracy and greatly increase the computational efficiency of these methods. In addition to improvements in the accuracy, our approach can greatly reduce the number of labeled pixels needed to achieve the same level of the accuracy based on randomly selected labeled pixels.
- Published
- 2023
47. Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks.
- Author
-
Chen, Zhiqian, Chen, Fanglan, Zhang, Lei, Ji, Taoran, Fu, Kaiqun, Zhao, Liang, Chen, Feng, Wu, Lingfei, Aggarwal, Charu, and Lu, Chang-Tien
- Published
- 2024
- Full Text
- View/download PDF
48. Local-Global Representation Enhancement for Multi-View Graph Clustering.
- Author
-
Zhao, Xingwang, Hou, Zhedong, and Wang, Jie
- Subjects
GRAPH algorithms ,K-means clustering - Abstract
In recent years, multi-view graph clustering algorithms based on representations learning have received extensive attention. However, existing algorithms are still limited in two main aspects, first, most algorithms employ graph convolution networks to learn the local representations, but the presence of high-frequency noise in these representations limits the clustering performance. Second, in the process of constructing a global representation based on the local representations, most algorithms focus on the consistency of each view while ignoring complementarity, resulting in lower representation quality. To address the aforementioned issues, a local-global representation enhancement for multi-view graph clustering algorithm is proposed in this paper. First, the low-frequency signals in the local representations are enhanced by a low-pass graph encoder, which yields smoother and more suitable local representations for clustering. Second, by introducing an attention mechanism, the local embedded representations of each view can be weighted and fused to obtain a global representation. Finally, to enhance the quality of the global representation, it is jointly optimized using the neighborhood contrastive loss and reconstruction loss. The final clustering results are obtained by applying the k-means algorithm to the global representation. A wealth of experiments have validated the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. Hierarchical graph learning with convolutional network for brain disease prediction.
- Author
-
Liu, Tong, Liu, Fangqi, Wan, Yingying, Hu, Rongyao, Zhu, Yongxin, and Li, Li
- Subjects
BRAIN diseases ,GRAPH neural networks ,FUNCTIONAL connectivity ,NEUROLOGICAL disorders ,DEEP learning - Abstract
In computer-aided diagnostic systems, the functional connectome approach has become a common method for detecting neurological disorders. However, the existing methods either ignore the uniqueness of different subjects across the functional connectivities or neglect the commonality of the same disease for the functional connectivity of each subject, resulting in a lack of capacity of capturing a comprehensive functional model. To solve the issues, we develop a hierarchical graph learning with convolutional network that not only considers the unique information of each subject, but also takes the common information across subjects into account. Specifically, the proposed method consists of two structures, one is the individual graph model which selects the representative brain regions by combining each subject feature and its related brain region-based graph. The other is the population graph model to directly conduct classification performance by updating the information of each subject which considers both the subject itself and the nearest neighbours. Experimental results indicate that the proposed method on four real datasets outperforms the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
50. AI-Empowered Multimodal Hierarchical Graph-Based Learning for Situation Awareness on Enhancing Disaster Responses.
- Author
-
Chen, Jieli, Seng, Kah Phooi, Ang, Li Minn, Smith, Jeremy, and Xu, Hanyue
- Subjects
SITUATIONAL awareness ,CONSCIOUSNESS raising ,CONVOLUTIONAL neural networks ,USER-generated content ,ACOUSTIC imaging ,FEATURE extraction ,SOCIAL media - Abstract
Situational awareness (SA) is crucial in disaster response, enhancing the understanding of the environment. Social media, with its extensive user base, offers valuable real-time information for such scenarios. Although SA systems excel in extracting disaster-related details from user-generated content, a common limitation in prior approaches is their emphasis on single-modal extraction rather than embracing multi-modalities. This paper proposed a multimodal hierarchical graph-based situational awareness (MHGSA) system for comprehensive disaster event classification. Specifically, the proposed multimodal hierarchical graph contains nodes representing different disaster events and the features of the event nodes are extracted from the corresponding images and acoustic features. The proposed feature extraction modules with multi-branches for vision and audio features provide hierarchical node features for disaster events of different granularities, aiming to build a coarse-granularity classification task to constrain the model and enhance fine-granularity classification. The relationships between different disaster events in multi-modalities are learned by graph convolutional neural networks to enhance the system's ability to recognize disaster events, thus enabling the system to fuse complex features of vision and audio. Experimental results illustrate the effectiveness of the proposed visual and audio feature extraction modules in single-modal scenarios. Furthermore, the MHGSA successfully fuses visual and audio features, yielding promising results in disaster event classification tasks. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
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