Graph Neural Networks

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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GOOD-D: On Unsupervised Graph Out-Of-Distribution Detection
Most existing machine learning models are trained based on the closed-world assumption, where the test data is assumed to be drawn …
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Contrastive Graph Similarity Networks
Graph similarity learning is a significant and fundamental issue in the theory and analysis of graphs, which has been applied in a …
Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs
Continuous-time dynamic graphs naturally abstract many real-world systems, such as social and transactional networks. While the …
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Pseudo-Riemannian Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally …
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Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination
Graph contrastive learning (GCL) alleviates the heavy reliance on label information for graph representation learning (GRL) via …
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