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cs.LG2024
SpeGCL: Self-supervised Graph Spectrum Contrastive Learning without Positive Samples
Yuntao Shou, Xiangyong Cao, Deyu Meng
Graph Contrastive Learning (GCL) excels at managing noise and fluctuations in input data, making it popular in various fields (e.g., social networks, and knowledge graphs). Our stu…
cs.LG2024
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck
Yuntao Shou, Haozhi Lan, Xiangyong Cao
Graph Neural Networks (GNNs) have received extensive research attention due to their powerful information aggregation capabilities. Despite the success of GNNs, most of them suffer…
cs.LG2024
Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis
Haodong Wen, Bodong Du, Ruixun Liu +2
Recently, the optimization of polynomial filters within Spectral Graph Neural Networks (GNNs) has emerged as a prominent research focus. Existing spectral GNNs mainly emphasize pol…