%0 Journal Article %1 citeulike:571785 %A Everett, Martin G. %A Borgatti, Stephen P. %D 1998 %J CONNECTIONS %K community %N 1 %P 49--61 %T Analyzing Clique Overlap %U http://www.analytictech.com/borgatti/papers/analyzing_clique_overlap.htm %V 21 %X There are a large number of techniques that try and determine areas within a network in which individuals are more closely linked to each other than outsiders. However, once these cohesive subgraphs have been identified researchers are often left with a long list of overlapping subgroups and have no means of assessing the structure or importance of these groups. In this paper we examine techniques for describing and reducing the amount of overlap so that the analyst can better understand the complex underlying clique structure.

@article{citeulike:571785, abstract = {There are a large number of techniques that try and determine areas within a network in which individuals are more closely linked to each other than outsiders. However, once these cohesive subgraphs have been identified researchers are often left with a long list of overlapping subgroups and have no means of assessing the structure or importance of these groups. In this paper we examine techniques for describing and reducing the amount of overlap so that the analyst can better understand the complex underlying clique structure.}, added-at = {2006-09-25T12:54:00.000+0200}, author = {Everett, Martin G. and Borgatti, Stephen P.}, biburl = {https://www.bibsonomy.org/bibtex/20782df777506e52e2f126d372947b86e/grahl}, citeulike-article-id = {571785}, comment = {analyses classic "cohesive subgroup" detections are overlapping (non-overlapping techniques are ignored here, because they are considered to place an odd contraint). This work does not include the community detection via community measure. The authors rely on clique detection in the social network, that identify many overlaps. Thus they extract a co-clique matrix, where the (i,j)th entry is the number of actors clique i and clique j have in common. This matrix is used as input to a hierarchical clustering routine (single link / average link) They introduce the clique overlap centrality measure, that captures the numbe of cliques an actor belongs to. This is correlated with degree and flow betweenness. quotes: "Indeed the methods which do not allow for overlap may be viewed as odd since they place an artificial contraint on the groupings they are seeking to uncover"}, interhash = {90101b5929edb6f9c9fe121a445538bf}, intrahash = {0782df777506e52e2f126d372947b86e}, journal = {CONNECTIONS}, keywords = {community}, number = 1, pages = {49--61}, priority = {0}, timestamp = {2006-09-25T12:54:00.000+0200}, title = {Analyzing Clique Overlap}, url = {http://www.analytictech.com/borgatti/papers/analyzing_clique_overlap.htm}, volume = 21, year = 1998 }