TY - GEN
T1 - A New Multi-objective Evolution Model for Community Detection in Multi-layer Networks
AU - Chen, Xuejiao
AU - Li, Xianghua
AU - Deng, Yue
AU - Chen, Siqi
AU - Gao, Chao
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In reality, many complex network systems can be abstracted to community detection in multi-layer networks, such as social relationships networks across multiple platforms. The composite community structure in multi-layer networks should be able to comprehensively reflect and describe the community structure of all layers. At present, most community detection algorithms mainly focus on the single layer networks, while those in multi-layer networks are still at the initial stage. In order to detect community structures in multi-layer networks, a new multi-objective evolution model is proposed in this paper. This model introduces the concept of modularity in different decision domains and the method of local search to iteratively optimize each layer of a network. Taking NSGA-II as the benchmark algorithm, the proposed multi-objective evolution model is applied to optimize the genetic operation and optimal solution selection strategies. The new algorithm is denoted as MulNSGA-II. The MulNSGA-II algorithm adopts the locus-based representation strategy, and integrates the genetic operation and local search. In addition, different optimal solution selection strategies are used to determine the optimal composite community structure. Experiments are carried out in real and synthetic networks, and results demonstrate the performance and effectiveness of the proposed model in multi-layer networks.
AB - In reality, many complex network systems can be abstracted to community detection in multi-layer networks, such as social relationships networks across multiple platforms. The composite community structure in multi-layer networks should be able to comprehensively reflect and describe the community structure of all layers. At present, most community detection algorithms mainly focus on the single layer networks, while those in multi-layer networks are still at the initial stage. In order to detect community structures in multi-layer networks, a new multi-objective evolution model is proposed in this paper. This model introduces the concept of modularity in different decision domains and the method of local search to iteratively optimize each layer of a network. Taking NSGA-II as the benchmark algorithm, the proposed multi-objective evolution model is applied to optimize the genetic operation and optimal solution selection strategies. The new algorithm is denoted as MulNSGA-II. The MulNSGA-II algorithm adopts the locus-based representation strategy, and integrates the genetic operation and local search. In addition, different optimal solution selection strategies are used to determine the optimal composite community structure. Experiments are carried out in real and synthetic networks, and results demonstrate the performance and effectiveness of the proposed model in multi-layer networks.
KW - Community detection
KW - Evolutionary algorithm
KW - Multi-layer networks
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85081542260&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-29551-6_18
DO - 10.1007/978-3-030-29551-6_18
M3 - 会议稿件
AN - SCOPUS:85081542260
SN - 9783030295509
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 197
EP - 208
BT - Knowledge Science, Engineering and Management - 12th International Conference, KSEM 2019, Proceedings
A2 - Douligeris, Christos
A2 - Apostolou, Dimitris
A2 - Karagiannis, Dimitris
PB - Springer
T2 - 12th International Conference on Knowledge Science, Engineering and Management, KSEM 2019
Y2 - 28 August 2019 through 30 August 2019
ER -