Fuzzy overlapping community quality metrics

被引：20

作者：

Chen M. ^{[1
]}

Szymanski B.K. ^{[1
]}

机构：

[1] Department of Computer Science, Rensselaer Polytechnic Institute, 110 8th Street, Troy, 12180, NY

来源：

Social Network Analysis and Mining | 2015年 / 5卷 / 01期

关键词：

Community Detection; Community Quality; Community Detection Algorithm; Synthetic Network; Saccharomyces Genome Database;

D O I：

10.1007/s13278-015-0279-8

中图分类号：

C [社会科学总论];

学科分类号：

030301 [社会学];

摘要：

Modularity is widely used to effectively measure the strength of the disjoint community structure found by community detection algorithms. Several overlapping extensions of modularity were proposed to measure the quality of overlapping community structure. However, all these extensions differ just in the way they define the belonging coefficient and belonging function. Yet, there is lack of systematic comparison of different extensions. To fill this gap, we overview overlapping extensions of modularity and generalize them with a uniform definition enabling application of different belonging coefficients and belonging functions to select the best. In addition, we extend localized modularity, modularity density, and eight local community quality metrics to enable their usages for overlapping communities. The experimental results on a large number of real networks and synthetic networks using overlapping extensions of modularity, overlapping modularity density, and local metrics show that the best results are obtained when the product of the belonging coefficients of two nodes is used as the belonging function. Moreover, the results may be used to guide researchers on which metrics to adopt when measuring the quality of overlapping community structure. © 2015, Springer-Verlag Wien.

引用

页码：1 / 14

页数：13

共 42 条

[1]

Adamic L.A., U.S. election: Divided they blog. In: Proceedings of the 3rd International Workshop on Link Discovery., LinkKDD ’05ACM, New York, pp 36–43, (2004)

[2]

Boguna M., Pastor-Satorras R., Diaz-Guilera A., Arenas A., Models of social networks based on social distance attachment, Phys Rev E, 70, 56, (2004)

[3]

Chakraborty T., Srinivasan S., Ganguly N., Mukherjee A., Bhowmick S., On the permanence of vertices in network communities. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14ACM, New York, pp 1396–1405, (2014)

[4]

Chen D., Shang M., Lv Z., Fu Y., Detecting overlapping communities of weighted networks via a local algorithm, Phys A Stat Mech Appl, 389, 19, pp. 4177-4187, (2010)

[5]

Chen M., Kuzmin K., Szymanski B., Community detection via maximization of modularity and its variants, IEEE Trans Comput Soc Syst, 1, 1, pp. 46-65, (2014)

[6]

IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp 856–863, (2014)

[7]

Chen M., Nguyen T., Szymanski B.K., A new metric for quality of network community structure, ASE Hum J, 2, 4, pp. 226-240, (2013)

[8]

Chen M., Nguyen T., Szymanski BK (2013b) On measuring the quality of a network community structure, Proceedings of ASE/IEEE International Conference on Social Computing, pp. 122-127

[9]

Collins S.R., Kemmeren K.P., Chu Zhao F.X., Greenblatt G.J.F., Spencer F., Toward a comprehensive atlas of the physical interactome of saccharomyces cerevisiae, Mol Cell Proteomic, 6, pp. 439-450, (2007)

[10]

Duch J., Arenas A., Community detection in complex networks using extremal optimization, Phys Rev E, 72, 27, (2005)

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