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This paper introduces LUCID, an LLM-guided interpretable and training-free unsupervised community detection method that uses a four-stage pipeline inspired by phase-transition kinetics to achieve state-of-the-art performance on real-world graphs.
An analysis of Twitter's community structure using domain co-occurrence data and PCA/t-SNE, revealing tight right-wing and diffuse left-wing clusters, with implications for recommender systems and random walk mixing times.
This paper proposes MPP-GNN, a Meta Probabilistic Pooling GNN that adaptively discovers subject-specific brain modules for fMRI-based Alzheimer's disease classification, achieving state-of-the-art AUC on two public datasets and aligning with canonical brain network organization.
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.
ModTGCN is a modularity-aware graph neural network that jointly optimizes cross-entropy and a modularity-based auxiliary objective to improve text classification by leveraging global community structure in document graphs, achieving consistent gains on five benchmarks.
Researchers from the University of British Columbia propose an unsupervised graph-based system for organizing arguments from online debates by constructing interaction graphs and applying community detection to reveal diverse viewpoint distributions. The approach requires no training data and aims to help users navigate complex argumentative landscapes and combat filter bubbles.