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Exploring Relational Semantics for Inductive Knowledge Graph Completion
Knowledge graph completion (KGC) aims to infer missing information in incomplete knowledge graphs (KGs). Most previous works only …
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Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most …
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Adapting Membership Inference Attacks to GNN for Graph Classification: Approaches and Implications
In light of the wide application of Graph Neural Networks (GNNs), Membership Inference Attack (MIA) against GNNs raises severe privacy …
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Hypergraph Convolutional Network for Group Recommendation
Group activities have become an essential part of people’s daily life, which stimulates the requirement for intensive research on the …
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ANEMONE: Graph Anomaly Detection with Multi-Scale Contrastive Learning
Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud …
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Leveraging Information Bottleneck for Scientific Document Summarization
Anomaly detection on graphs plays a significant role in various domains, including cybersecurity, e-commerce, and financial fraud …
Projective Ranking: A Transferable Evasion Attack Method on Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a series of effective learning methods for graph-related tasks. However, GNNs are shown …
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iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients
Differentiable ARchiTecture Search(DARTS) has recently become the mainstream in the neural architecture search (NAS) due to its …
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Medical Code Assignment with Gated Convolution and Note-Code Interaction
Medical code assignment from clinical text is a fundamental task in clinical information system management. As medical notes are …
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Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Graph representation learning plays a vital role in processing graph-structured data. However, prior arts on graph representation …
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