Open AccessAn Efficient and Adaptive Method for Overlapping Community Detection in Real‐World Networks
Author(s) -
Sun Liping,
Liu Jun,
Zheng Xiaoyao,
Luo Yonglong
Publication year - 2018
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2017.09.012
Subject(s) - computer science , selection (genetic algorithm) , set (abstract data type) , task (project management) , pagerank , data mining , artificial intelligence , machine learning , algorithm , pattern recognition (psychology) , theoretical computer science , management , economics , programming language
In real‐world networks, nodes may belong to more than one community simultaneously. Overlapping community detection in complex networks is a challenging task. An adaptive overlapping community detection method based on seed selection and expansion is proposed. Depending on the restrictions on the seed selection stage, a set of seeds is generated without specified set size. The personalized PageRank algorithm is used to evaluate the community for seed expansion. The uncovered nodes could be adaptively allocated to the appropriate clusters. A thorough comparison between the proposed method and other overlapping community detection methods considered is provided to indicate the effectiveness of the former. The experimental results demonstrate that the presented method is effective.