A local information based multi-objective evolutionary algorithm for community detection in complex networks
| Autor: | Xingyi Zhang, Yunyun Niu, Yansen Su, Fan Cheng, Tingting Cui |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science business.industry media_common.quotation_subject Evolutionary algorithm 02 engineering and technology Complex network Machine learning computer.software_genre 03 medical and health sciences 030104 developmental biology Line (geometry) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Artificial intelligence business computer Software media_common |
| Zdroj: | Applied Soft Computing. 69:357-367 |
| ISSN: | 1568-4946 |
| Popis: | Due to the important role in analyzing the structure and function of complex networks, community detection has attracted increasing attention in the past years. Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in community detection and, in this paper, we continue this research line by further exploring the potential of MOEAs in detecting communities. To be specific, a local information based MOEA, termed LMOEA, is proposed for community detection, where an individual updating strategy is suggested to improve the quality of community detection. Considering that a network often contains some local communities which are easily detected in the early evolutions, the proposed strategy utilizes these local communities found by individuals to guide the search in the following generations. The effectiveness of the proposed LMOEA is verified by comparing it with several existing evolutionary algorithms for community detection on both synthetic and real-world networks. Experimental results demonstrate the competitiveness of the proposed LMOEA for community detection in complex networks. |
| Databáze: | OpenAIRE |
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