arXiv Machine Learning
G-NAC: Graph Neural Automata Clustering via Emergent Domain Formation
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Learning and Clustering on Temporal Graphs: Principles, Primitives, and Pooling
arXiv:2608. 03696v1 Announce Type: new Abstract: This work focuses on the problem of learning on temporal graphs, with particular emphasis on the task of clustering: obtaining coarse-grained representations by aggregating information from nodes, edges, and temporal dynamics - a task related to pooling in machine learning on graphs, or community detection in network science.
arXiv AI
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Towards One-for-All Foundation Model for Attributed Graph Clustering
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Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
arXiv Machine Learning
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Bridging Academia and Industry: A Comprehensive Benchmark for Attributed Graph Clustering
arXiv:2602.08519v2 Announce Type: replace Abstract: Attributed Graph Clustering (AGC) is a fundamental unsupervised task that integrates structural topology and node attributes to uncover latent patt...
arXiv Machine Learning
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Robust Contrastive Graph Clustering with Adaptive Local-Global Integration
arXiv:2605. 28209v2 Announce Type: replace Abstract: Graph clustering is essential in graph analysis for revealing structural patterns and node communities.
arXiv Machine Learning
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Towards Adaptive Federated Graph Clustering: A Global Community-aware Contrastive Learning-based Approach
arXiv:2609.26063v1 Announce Type: new Abstract: Federated graph learning (FGL) enables multiple clients to collaboratively train graph models without sharing their private graph data, providing a pro...