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Faster Clustering Coefficient Using Vertex Covers

机译:使用顶点覆盖更快的聚类系数

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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. d2max), 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, hatV subseteq 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(hatV. hatd_max^2 + t_VC) where hatd_max is the size of the largest adjacency list in the vertex cover and t_VC 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.
机译:聚类系数(也称为三角形计数)是一种广泛使用的图分析,用于测量顶点聚在一起的紧密度。直观地,可以将聚类系数视为普通朋友与一个人在社交网络中可能拥有的所有可能联系的比率。用于计算聚类系数的最广为人知的时间复杂度是使用邻接列表交集,并且为O(V。d2max),其中dmax是图中所有顶点中最大邻接列表的大小。在这项工作中,我们展示了一种通过利用顶点覆盖hatVsubseteq V来计算无向图和无权图中的聚类系数的新颖方法。这种新方法减少了三角形计算的次数。每个三角形3次。新算法的复杂度为O(hatV。hatd_max ^ 2 + t_VC),其中hatd_max是顶点覆盖范围内最大邻接列表的大小,而t_VC是查找顶点覆盖范围所需的时间。即使对于简单的顶点覆盖算法,这也可以减少10-30%的执行时间,同时计算出三角形(3回路)的确切数量。我们扩展了顶点覆盖的使用,以支持对动态图进行计数平方(4电路)和聚类系数。

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