Finding local community structure in networks
Finding local community structure in networks
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DOI：
10.1103/physreve.72.026132
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发表时间：
2005-08-01
影响因子：
2.4
通讯作者：
Clauset, A
中科院分区：
文献类型：
--
作者：
Clauset, A
虽然网络中的全局社区结构的推断最近已经成为物理界非常感兴趣的话题，但所有此类算法都要求图是完全已知的。在这里，我们定义了一个衡量当地社区结构和算法，推断出社区的层次结构，包围一个给定的顶点，探索图形一个顶点的时间。对于一般的图，当d是平均度，k是要探索的顶点数时，该算法的时间复杂度为O（k（2）d）。对于探索一个新顶点是耗时的图，运行时间是线性的，O（k）。我们表明，在计算机生成的图形上，这种技术的平均行为近似于需要全局知识的算法。作为一个应用，我们使用该算法提取有意义的局部聚类信息的大型推荐网络的在线零售商。
Although the inference of global community structure in networks has recently become a topic of great interest in the physics community, all such algorithms require that the graph be completely known. Here, we define both a measure of local community structure and an algorithm that infers the hierarchy of communities that enclose a given vertex by exploring the graph one vertex at a time. This algorithm runs in time O(k(2)d) for general graphs when d is the mean degree and k is the number of vertices to be explored. For graphs where exploring a new vertex is time consuming, the running time is linear, O(k). We show that on computer-generated graphs the average behavior of this technique approximates that of algorithms that require global knowledge. As an application, we use this algorithm to extract meaningful local clustering information in the large recommender network of an online retailer.