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cs.LG2025
Jensen-Shannon Divergence Message-Passing for Rich-Text Graph Representation Learning
Zuo Wang, Ye Yuan
In this paper, we investigate how the widely existing contextual and structural divergence may influence the representation learning in rich-text graphs. To this end, we propose Je…
cs.LG2025
A Multi-scale Fused Graph Neural Network with Inter-view Contrastive Learning for Spatial Transcriptomics Data Clustering
Jianping Mei, Siqi Ai, Ye Yuan
Spatial transcriptomics enables genome-wide expression analysis within native tissue context, yet identifying spatial domains remains challenging due to complex gene-spatial intera…
cs.LG2025
Condensation-Concatenation Framework for Dynamic Graph Continual Learning
Tingxu Yan, Ye Yuan
Dynamic graphs are prevalent in real-world scenarios, where continuous structural changes induce catastrophic forgetting in graph neural networks (GNNs). While continual learning h…