DocumentCode :
660796
Title :
Faster Clustering Coefficient Using Vertex Covers
Author :
Green, Oded ; Bader, David A.
Author_Institution :
Coll. of Comput., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2013
fDate :
8-14 Sept. 2013
Firstpage :
321
Lastpage :
330
Abstract :
Clustering coefficients, also called triangle counting, is a widely-used graph analytic for measuring the closeness in which vertices cluster together. Intuitively, clustering coefficients can be thought of as the ratio of common friends versus all possible connections a person might have in a social network. The best known time complexity for computing clustering coefficients uses adjacency list intersection and is O(V · dmax2), where dmax is the size of the largest adjacency list of all the vertices in the graph. In this work, we show a novel approach for computing the clustering coefficients in an undirected and unweighted graphs by exploiting the use of a vertex cover, V̂ ⊆ V. This new approach reduces the number of times that a triangle is counted by as many as 3 times per triangle. The complexity of the new algorithm is O(V̂ · ĥmax2 + tVC) where d̂max is the size of the largest adjacency list in the vertex cover and tVC is the time needed for finding the vertex cover. Even for a simple vertex cover algorithm this can reduce the execution time 10-30% while counting the exact number of triangles (3-circuits). We extend the use of the vertex cover to support counting squares (4-circuits) and clustering coefficients for dynamic graphs.
Keywords :
computational complexity; graph theory; network theory (graphs); pattern clustering; social networking (online); adjacency list intersection; algorithm complexity; closeness measurement; clustering coefficient; counting squares; dynamic graph; graph analytic; graph vertices; social network; time complexity; triangle counting; undirected graph; unweighted graph; vertex covers; Algorithm design and analysis; Approximation methods; Clustering algorithms; Heuristic algorithms; Social network services; Time complexity; Graph Algorithms; Network Science; Social Network Analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Computing (SocialCom), 2013 International Conference on
Conference_Location :
Alexandria, VA
Type :
conf
DOI :
10.1109/SocialCom.2013.51
Filename :
6693348
Link To Document :
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