|International Computer Science and Engineering Society (ICSES)|
International Transactions on Data Science, Engineering and Technology
Vol. 3, No. 1, Mar. 2020
Complex Networks, Communities and Fuzzy Structures | Miscellaneous
Hui-Jia Li a,, Hui-Dong Wu b
Highlights and Novelties
Community detection has many potential applications from Computer Science to Biology. The main purpose of community detection is to unveil the community structure of the network. Having the community structure, one can understand the functional properties of the network. In this paper, the most important issues on community detection problem were reviewed, including the properties of community structure in complex networks, an important type of communities, i.e. the fuzzy communities and the applications and challenges of community detection works. After these explanations of the nature of the community structure in complex network, we can have a clear landscape of the growth of community detection technology.On the other hand, the quality measures are used where the true communities are not available and they estimate how much a partition is meaningful with respect to other partitions. Finally, in order to compare the performance of the algorithms, one needs to test them on some benchmark graph. So far, only few benchmark networks have been proposed for evaluating community detection methods. The most prevalent one are the GN, the LFR, and the ring of cliques benchmark graphs.
© Copyright was transferred to International Computer Science and Engineering Society (ICSES) by all the Authors.
Cite this manuscript as
Hui-Jia Li, Hui-Dong Wu, "Complex Networks, Communities and Fuzzy Structures," International Transactions on Data Science, Engineering and Technology (ITDSET), vol. 3, no. 1, pp. 1-4, Mar. 2020.
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