graph neural networks

Demystifying Uneven Vulnerability of Link Stealing Attacks against Graph Neural Networks
While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in real-world applications, they have been shown …
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Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs
Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophilic-prone or …
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Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completion
Knowledge graphs (KGs), as a structured form of knowledge representation, have been widely applied in the real world. Recently, …
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Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
Recent studies focus on formulating the traffic forecasting as a spatio-temporal graph modeling problem. They typically construct a …
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Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Graph neural architecture search (NAS) has gained popularity in automatically designing powerful graph neural networks (GNNs) with …
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Robust Graph Representation Learning for Local Corruption Recovery
The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a …
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Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating
Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several …
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Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphs
Link prediction on dynamic graphs is an important task in graph mining. Existing approaches based on dynamic graph neural networks …
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Neighbor Contrastive Learning on Learnable Graph Augmentation
Recent years, graph contrastive learning (GCL), which aims to learn representations from unlabeled graphs, has made great progress. …
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Simple and Efficient Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to embed rich structural and semantic information of a …
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