Titlebook: Web and Big Data; 8th International Jo Wenjie Zhang,Anthony Tung,Hongjie Guo Conference proceedings 2024 The Editor(s) (if applicable) and

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Automated Multi-scale Contrastive Learning with?Sample-Awareness for?Graph Classificationopology of the input graph and refine neighborhood information. Extensive experiments on eight benchmark datasets demonstrate that our proposed SaMGCL achieves superior graph classification performance compared to the current state-of-the-art approaches.

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CGAR: A Contrastive Graph Attention Residual Network for?Enhanced Fake News Detection Additionally, the integration of contrastive learning into the loss function enables the model to explicitly differentiate between conversational threads of identical and distinct classes, thereby addressing the challenge of class imbalance by emphasizing sample similarities. Empirical evaluations

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LPRL-GCNN for?Multi-relation Link Prediction in?Educationof judging what kind of relationship. This paper proposes a new link prediction model based on representation learning, namely LPRL-GCNN model - LPRL-GCNN (Link Prediction model based on Representation Learning). This model can not only predict new knowledge concept associations, but also predict th

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MERGE: Multi-view Relationship Graph Network for?Event-Driven Stock Movement Predictionem. MERGE involves a Multi-View Relationship Graph Network module that constructs multiple dynamic graphs by mining relational information in prices to model the various types of stock interactions in the market from different perspectives. In addition, to sufficiently consider the impact of externa

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Relation-Aware Heterogeneous Graph Neural Network for?Fraud Detection features and topology information for GNNs, allowing for precise and scalable fraud detection. Specifically, we first use a relation-aware node map-reduce to preprocess the computational graph. Then we use the hybrid propagation scheme, which optimizes the collection of neighborhood nodes with redu

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Relation-Aware Heterogeneous Graph Neural Network for?Fraud Detection features and topology information for GNNs, allowing for precise and scalable fraud detection. Specifically, we first use a relation-aware node map-reduce to preprocess the computational graph. Then we use the hybrid propagation scheme, which optimizes the collection of neighborhood nodes with redu