graph neural networks

Learning Strong Graph Neural Networks with Weak Information
Graph Neural Networks (GNNs) have exhibited impressive performance in many graph learning tasks. Nevertheless, the performance of GNNs …
G2Pxy: Generative Open-Set node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural …
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 …
Finding the Missing-half: Graph Complementary Learning
Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophilic-prone or …
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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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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CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing
Graph neural networks are a useful tool to handle graph data. However, existing graph neural networks face the challenges of …
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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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