2 citations · 4 across the 9 of their papers we have counts for
4 papers · 1 filter
CellCLAT: Preserving Topology and Trimming Redundancy in Self-Supervised Cellular Contrastive Learning
Bin Qin, Qirui Ji, Jiangmeng Li +4
Self-supervised topological deep learning (TDL) represents a nascent but underexplored area with significant potential for modeling higher-order interactions in simplicial complexe…
Introducing Diminutive Causal Structure into Graph Representation Learning
Hang Gao, Peng Qiao, Yifan Jin +3
When engaging in end-to-end graph representation learning with Graph Neural Networks (GNNs), the intricate causal relationships and rules inherent in graph data pose a formidable c…
Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective
Qirui Ji, Jiangmeng Li, Jie Hu +3
Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the str…
M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning
Yuanyuan Guo, Yu Xia, Rui Wang +3
Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks.…