%0 Generic %1 nepusz07b %A Nepusz, Tamás %A Petróczi, Andrea %A Négyessy, László %A Bazsó, Fülöp %D 2007 %K cluster communities community index measure overlapping %T Fuzzy communities and the concept of bridgeness in complex networks %U http://www.arxiv.org/abs/0707.1646 %X We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time with exact numerical degrees of membership, even in the presence of uncertainty in the data being analyzed. We created an algorithm for determining the optimal degrees of membership with respect to a given goal function. Based on the degrees of membership, we introduce a new measure that is able to identify outlier vertices that do not really belong to any of the communities, bridge vertices belonging significantly to more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to social networks, scientific collaboration networks and cortical networks with high confidence.

@misc{nepusz07b, abstract = {We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time with exact numerical degrees of membership, even in the presence of uncertainty in the data being analyzed. We created an algorithm for determining the optimal degrees of membership with respect to a given goal function. Based on the degrees of membership, we introduce a new measure that is able to identify outlier vertices that do not really belong to any of the communities, bridge vertices belonging significantly to more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to social networks, scientific collaboration networks and cortical networks with high confidence.}, added-at = {2011-08-05T23:55:42.000+0200}, author = {Nepusz, Tamás and Petróczi, Andrea and Négyessy, László and Bazsó, Fülöp}, biburl = {https://www.bibsonomy.org/bibtex/2a82ecb64f5f8083b10492cf67e106334/folke}, howpublished = {E-print}, interhash = {2b3a0bec3867d62ac5204aea90a9cd46}, intrahash = {a82ecb64f5f8083b10492cf67e106334}, keywords = {cluster communities community index measure overlapping}, timestamp = {2011-08-05T23:55:42.000+0200}, title = {Fuzzy communities and the concept of bridgeness in complex networks}, url = {http://www.arxiv.org/abs/0707.1646}, year = 2007 }