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04/20/2023
ID-MixGCL: Identity Mixup for Graph Contrastive Learning
Recently developed graph contrastive learning (GCL) approaches compare t...
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02/23/2023
Auto-HeG: Automated Graph Neural Network on Heterophilic Graphs
Graph neural architecture search (NAS) has gained popularity in automati...
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01/14/2023
A Comprehensive Survey of Graph-level Learning
Graphs have a superior ability to represent relational data, like chemic...
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06/25/2022
Geometry Contrastive Learning on Heterogeneous Graphs
Self-supervised learning (especially contrastive learning) methods on he...
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05/31/2022
Graph-level Neural Networks: Current Progress and Future Directions
Graph-structured data consisting of objects (i.e., nodes) and relationsh...
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02/25/2022
Projective Ranking-based GNN Evasion Attacks
Graph neural networks (GNNs) offer promising learning methods for graph-...
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06/06/2021
Semi-Riemannian Graph Convolutional Networks
Graph Convolutional Networks (GCNs) are typically studied through the le...
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05/12/2021
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation Learning
Graph representation learning plays a vital role in processing graph-str...
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02/26/2021
Task-adaptive Neural Process for User Cold-Start Recommendation
User cold-start recommendation is a long-standing challenge for recommen...
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10/23/2020
Graph Geometry Interaction Learning
While numerous approaches have been developed to embed graphs into eithe...
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05/17/2020
Deep Learning for Community Detection: Progress, Challenges and Opportunities
As communities represent similar opinions, similar functions, similar pu...
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06/18/2019
Deep Active Learning for Anchor User Prediction
Predicting pairs of anchor users plays an important role in the cross-ne...
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04/22/2019
