Abstract
Community structure is one of the most important properties in complex networks. Detecting such communities plays an important role in a wide range of applications such as information sharing and diffusion, recommendation, and classification. In this paper, we propose a novel iterated greedy algorithm for detecting communities in complex networks. The algorithm is based on an iterative process that combines a destruction phase and a reconstruction phase. During the destruction phase, the algorithm destructs community indexes of a certain percent nodes having lower modularity contribution. In the reconstruction phase, their community indexes are reconstructed using the well-known Louvain construction heuristic. A local search procedure can be applied after the reconstruction phase to improve the performance of the algorithm. Experiments on the computer-generated networks and real-world networks show that our algorithm is very efficient and competitive compared with several state-of-the-art methods.
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Data availability
The synthetic networks and five real-world networks, including Chesapeake Bay, C. elegans, Erdos971, E-mail, and NetScience networks, have been deposited in the IG_DATASET repository, [https://github.com/wenquanli/IG_DATASET]. The com-DBLP network and com-Amazon network are openly available from the Stanford Large Network Dataset Collection at [https://snap.stanford.edu/data]. Other real-world networks used to support the findings of this study are available in the Newman repository, [https://www-personal.umich.edu/~mejn/netdata].
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This work is supported by the Natural Science Foundation of Shandong Province, China [No. ZR2015AM015].
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Li, W., Kang, Q., Kong, H. et al. A novel iterated greedy algorithm for detecting communities in complex network. Soc. Netw. Anal. Min. 10, 29 (2020). https://doi.org/10.1007/s13278-020-00641-y
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DOI: https://doi.org/10.1007/s13278-020-00641-y