AI RESEARCH
HyReaL: Clustering Attributed Graph via Hyper-Complex Space Representation Learning
arXiv CS.LG
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ArXi:2411.14727v3 Announce Type: replace Clustering complex data in the form of attributed graphs has attracted increasing attention, where powerful graph representation is a critical prerequisite. However, the well-known Over-Smoothing (OS) effect makes Graph Convolutional Networks tend to homogenize the representation of graph nodes, while the existing OS solutions focus on alleviating the homogeneity of nodes' embeddings from the aspect of graph topology information, which is inconsistent with the attributed graph clustering objective. Therefore, we