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About this paper
Xiaoyang Lin, Renchi Yang, Yuqi Chen, Xin Huang
S³GLC clusters unlabeled graphs by combining GIN-based local representations with spectral descriptions of global topology, then trains soft assignments toward a balanced optimal-transport target. Its spectral features use top eigenvalues, while the balance constraint applies to soft targets rather than guaranteeing equal-sized hard clusters. On REDDIT-12K, it reports 37.8 ± 0.6% ACC, versus DGLC’s 23.2 ± 0.6%; gains are not universal.
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