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论文提出优化的 MCES 距离计算方法，使结构不相交的数据集划分可在大规模分子库上可行，并发现 MassSpecGym 等基准中单一连通分量占库的 95.5%，导致测试集系统性地偏向更大更复杂的分子，混淆了结构新颖性与外推难度。
The paper introduces Selective Hypergraph Refinement (SHR), a label-free post-processing method that uses hypergraphs to refine cluster assignments in frozen graph models without updating parameters, showing measurable improvements.
This paper proposes RHEA, a reliability-aware framework for multimodal-attributed graph clustering that estimates node-specific modality reliability from neighborhood consensus, reconstructs unreliable modalities, and uses reliability-aware fusion and optimal transport clustering. Experiments on four benchmarks show consistent gains, especially under noisy or missing attributes.
This paper proposes SCISE, a scalable unsupervised graph clustering framework that uses community-aware sampling and structural entropy to overcome structural isolation in mini-batch training, achieving state-of-the-art results on benchmark datasets.
Introduces SNMPBB, a nonmonotone gradient-based algorithm for symmetric nonnegative matrix factorization that achieves significant speedups over existing methods, with extensions to graph clustering and low-rank approximations.