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cs.LG2026
Dynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Quanxin Wang, Xuanting Xie, Bingheng Li +4
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a…
cs.LG2026
Beyond Missing Rates: Rethinking Incomplete Multi-View Clustering with Protocol Divergence
Haolu Liu, Xiyue Wang, Xuanting Xie +2
Incomplete multi-view clustering (IMVC) is typically evaluated by retraining separate models under different missing-view configurations. Evaluations indexed only by nominal missin…
cs.AI2026
Clustering as Reasoning: A -Means Interpretation of Chain-of-Thought Graph Learning
Xuanting Xie, Zhaochen Guo, Bingheng Li +4
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-…