Finding weighted \(k\)-truss communities in large networks

DOI10.1016/j.ins.2017.07.012zbMath1444.91180OpenAlexW2735638351MaRDI QIDQ778465

Tan Jin, Fanghua Ye, Zibin Zheng, Guohui Ling, Rong-Hua Li

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2017.07.012

zbMATH Keywords

weighted networkscommunity searchweighted \(k\)-truss community

Mathematics Subject Classification ID

Social networks; opinion dynamics (91D30)

Related Items (5)

Identifying structural hole spanners to maximally block information propagation ⋮ Top-\(k\) team synergy problem: capturing team synergy based on C3 ⋮ Overlapping community detection with least replicas in complex networks ⋮ OCSM: finding overlapping cohesive subgraphs with minimum degree ⋮ Finding influential communities in networks with multiple influence types

Uses Software

• Algorithm 457

Cites Work

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• The worst-case time complexity for generating all maximal cliques and computational experiments
• Main-memory triangle computations for very large (sparse (power-law)) graphs
• A note on the problem of reporting maximal cliques
• Classifying molecular sequences using a linkage graph with their pairwise similarities
• Efficient and effective community search
• A graph‐theoretic generalization of the clique concept
• The Structure and Function of Complex Networks
• Algorithm 457: finding all cliques of an undirected graph

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