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Adversarial attacks on Graph Neural Networks (GNNs) have emerged as a significant threat to the security of graph learning. Compared with Graph Modification Attacks (GMAs), Graph Injection Attacks (GIAs) are considered more realistic attacks, in which attackers perturb GNN models by injecting a small number of fake nodes. However, most existing black-box GIA methods either require comprehensive knowledge of the dataset and the ground-truth labels or a large number of queries to execute the attack, which is often unfeasible in many scenarios. In this paper, we propose an unsupervised method for leveraging the rich knowledge contained in the graph data themselves to enhance the success rate of graph injection attacks on the initial query. Specifically, we introduce GraphContrastive Learning-based Graph Injection Attack (GCIA), which consists of a node encoder, a reward predictor, and a fake node generator. The Graph Contrastive Learning (GCL)-based node encoder transforms nodes for low-dimensional continuous embedding, the reward predictor acts as a simplified surrogate for the target model, and the fake node generator produces fake nodes and edges based on several carefully designed loss functions, utilizing the node encoder and reward predictor. Extensive results demonstrate that the proposed GCIA method achieves a first query success rate of 91.2% on the Reddit dataset and improves the success rate to over 99.7% after 10 queries. [ABSTRACT FROM AUTHOR]

Trigger–action programming (TAP) enables users to automate Internet of Things (IoT) devices by creating rules such as "IF Device1.TriggerState is triggered, THEN Device2.ActionState is executed". As the number of IoT devices grows, the combination space between the functions provided by devices expands, making manual rule creation time-consuming for end-users. Existing TAP recommendation systems enhance the efficiency of rule discovery but face two primary issues: they ignore the association of rules between users and fail to model collaborative information among users. To address these issues, this article proposes a graph contrastive learning-based recommendation system for TAP rules, named GCL4TAP. In GCL4TAP, we first devise a data partitioning method called DATA2DIV, which establishes cross-user rule relationships and is represented by a user–rule bipartite graph. Then, we design a user–user graph to model the similarities among users based on the categories and quantities of devices that they own. Finally, these graphs are converted into low-dimensional vector representations of users and rules using graph contrastive learning techniques. Extensive experiments conducted on a real-world smart home dataset demonstrate the superior performance of GCL4TAP compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback–Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) are crucial tools for processing non-Euclidean data. However, due to scalability issues caused by the dependency and topology of graph data, deploying GNNs in practical applications is challenging. Some methods aim to address this issue by transferring GNN knowledge to MLPs through knowledge distillation. However, distilled MLPs cannot directly capture graph structure information and rely only on node features, resulting in poor performance and sensitivity to noise. To solve this problem, we propose a lightweight optimization method for GNNs that combines graph contrastive learning and variable-temperature knowledge distillation. First, we use graph contrastive learning to capture graph structural representations, enriching the input information for the MLP. Then, we transfer GNN knowledge to the MLP using variable temperature knowledge distillation. Additionally, we enhance both node content and structural features before inputting them into the MLP, thus improving its performance and stability. Extensive experiments on seven datasets show that the proposed KDGCL model outperforms baseline models in both transductive and inductive settings; in particular, the KDGCL model achieves an average improvement of 1.63% in transductive settings and 0.8% in inductive settings when compared to baseline models. Furthermore, KDGCL maintains parameter efficiency and inference speed, making it competitive in terms of performance. [ABSTRACT FROM AUTHOR]

Video summarization aims to analyze the structure and content of videos and extract key segments to construct summarization that can accurately summarize the main content, allowing users to quickly access the core information without browsing the full video. However, existing methods have difficulties in capturing long-term dependencies when dealing with long videos. On the other hand, there is a large amount of noise in graph structures, which may lead to the influence of redundant information and is not conducive to the effective learning of video features. To solve the above problems, we propose a video summarization generation network based on dynamic graph contrastive learning and feature fusion, which mainly consists of three modules: feature extraction, video encoder, and feature fusion. Firstly, we compute the shot features and construct a dynamic graph by using the shot features as nodes of the graph and the similarity between the shot features as the weights of the edges. In the video encoder, we extract the temporal and structural features in the video using stacked L-G Blocks, where the L-G Block consists of a bidirectional long short-term memory network and a graph convolutional network. Then, the shallow-level features are obtained after processing by L-G Blocks. In order to remove the redundant information in the graph, graph contrastive learning is used to obtain the optimized deep-level features. Finally, to fully exploit the feature information of the video, a feature fusion gate using the gating mechanism is designed to fully fuse the shallow-level features with the deep-level features. Extensive experiments are conducted on two benchmark datasets, TVSum and SumMe, and the experimental results show that our proposed method outperforms most of the current state-of-the-art video summarization methods. [ABSTRACT FROM AUTHOR]

Due to the success observed in deep neural networks with contrastive learning, there has been a notable surge in research interest in graph contrastive learning, primarily attributed to its superior performance in graphs with limited labeled data. Within contrastive learning, the selection of a "view" dictates the information captured by the representation, thereby influencing the model's performance. However, assessing the quality of information in these views poses challenges, and determining what constitutes a good view remains unclear. This paper addresses this issue by establishing the definition of a good view through the application of graph information bottleneck and structural entropy theories. Based on theoretical insights, we introduce CtrlGCL, a novel method for achieving a beneficial view in graph contrastive learning through coding tree representation learning. Extensive experiments were conducted to ascertain the effectiveness of the proposed view in unsupervised and semi-supervised learning. In particular, our approach, via CtrlGCL-H, yields an average accuracy enhancement of 1.06% under unsupervised learning when compared to GCL. This improvement underscores the efficacy of our proposed method. [ABSTRACT FROM AUTHOR]

This paper proposes colaGAE, a self-supervised learning framework for graph-structured data. While graph autoencoders (GAEs) commonly use graph reconstruction as a pretext task, this simple approach often yields poor model performance. To address this issue, colaGAE employs mutual isomorphism as a pretext task for a continuous latent space sampling GAE (colaGAE). The central idea of mutual isomorphism is to sample from multiple views in the latent space and reconstruct the graph structure, with significant improvements in terms of the model's training difficulty. To investigate whether continuous latent space sampling can enhance GAEs' learning of graph representations, we provide both theoretical and empirical evidence for the benefits of this pretext task. Theoretically, we prove that mutual isomorphism can offer improvements with respect to the difficulty of model training, leading to better performance. Empirically, we conduct extensive experiments on eight benchmark datasets and achieve four state-of-the-art (SOTA) results; the average accuracy rate experiences a notable enhancement of 0.3 % , demonstrating the superiority of colaGAE in node classification tasks. [ABSTRACT FROM AUTHOR]

User alignment can associate multiple social network accounts of the same user. It has important research implications. However, the same user has various behaviors and friends across different social networks. This will affect the accuracy of user alignment. In this paper, we aim to improve the accuracy of user alignment by reducing the semantic gap between the same user in different social networks. Therefore, we propose a semantically enhanced social network user alignment algorithm (SENUA). The algorithm performs user alignment based on user attributes, user-generated contents (UGCs), and user check-ins. The interference of local semantic noise can be reduced by mining the user's semantic features for these three factors. In addition, we improve the algorithm's adaptability to noise by multi-view graph-data augmentation. Too much similarity of non-aligned users can have a large negative impact on the user-alignment effect. Therefore, we optimize the embedding vectors based on multi-headed graph attention networks and multi-view contrastive learning. This can enhance the similar semantic features of the aligned users. Experimental results show that SENUA has an average improvement of 6.27% over the baseline method at hit-precision30. This shows that semantic enhancement can effectively improve user alignment. [ABSTRACT FROM AUTHOR]

Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases. [ABSTRACT FROM AUTHOR]