Maximum Likelihood Estimation on Directed Stochastic Blockmodels for Community Detection in Directed Graphs
The core message of this paper is to formulate directed graph clustering as a maximum likelihood estimation (MLE) problem on the directed stochastic block model (DSBM), and to derive efficient and interpretable clustering algorithms based on this statistical estimation framework.
Provable Filter for Real-world Graph Clustering: A Novel Approach with Theoretical Support
The author presents a novel solution for real-world graph clustering, addressing the limitations of existing methods by incorporating homophilic and heterophilic edges. By leveraging neighbor information, the proposed method outperforms state-of-the-art clustering techniques.
Deep Temporal Graph Clustering: A General Framework for Enhanced Representation Learning
The author introduces a general framework, TGC, for deep temporal graph clustering to address the limitations of existing methods in capturing dynamic interaction information in temporal graphs.