graph neural networks

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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Learning Graph Representations with Maximal Cliques
Non-Euclidean property of graph structures has faced interesting challenges when deep learning methods are applied. Graph convolutional …
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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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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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Deep Neighbor-aware Embedding for Node Clustering in Attributed Graphs
Node clustering aims to partition the vertices in a graph into multiple groups or communities. Existing studies have mostly focused on …
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Cyclic label propagation for graph semi-supervised learning
Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised …
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OpenWGL: Open-World Graph Learning for Unseen Class Node Classification
Graph learning, such as node classification, is typically carried out in a closed-world setting. A number of nodes are labeled, and the …
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Temporal Network Embedding for Link Prediction via VAE joint Attention Mechanism
Network representation learning or embedding aims to project the network into a low-dimensional space that can be devoted to different …
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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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