Distributed Pseudo-Likelihood Method for Efficient Community Detection in Large Networks
This paper introduces DPL, a distributed algorithm designed to efficiently detect community structures within large-scale networks by leveraging a block-wise splitting method and pseudo-likelihood estimation, significantly reducing computational complexity while maintaining accuracy.
Distributed Community Detection Algorithm for Large Networks with Grouped Community Structure
This research paper proposes a novel distributed community detection algorithm for large networks that leverages the inherent "grouped community structure" often found in real-world networks to improve computational efficiency without sacrificing accuracy.
Community Detection in Directed Networks with Missing Edges Using a Modified Flow Stability Algorithm
Incorporating known uncertainty levels, such as missing link information, into community detection algorithms like Flow Stability enhances the accuracy and robustness of community detection in networks with incomplete data.
Graph Contrastive Learning with Structure Semantics (GCLS$^2$) for Community Detection
This paper proposes GCLS$^2$, a novel graph contrastive learning framework that leverages structure semantics to improve community detection in graph data.
Efficient Community Detection in Networks via Local Dominance
The core message of this article is that the notion of local dominance can be used to efficiently detect communities in complex networks. The proposed Local Search (LS) algorithm identifies community centers based on local information, such as node degree and distance to other local leaders, and then assigns nodes to communities based on these local dominance relations.
VEC-SBM: Optimale Community Detection mit Vektoriellen Kanten
Die Integration von Kanteninformationen verbessert signifikant die Genauigkeit der Community Detection.
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