Tolerating the community detection resolution limit with edge weighting
Tolerating the community detection resolution limit with edge weighting
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DOI：
10.1103/physreve.83.056119
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发表时间：
2011-05-25
影响因子：
2.4
通讯作者：
Phillips, Cynthia A.
中科院分区：
文献类型：
--
作者：
Berry, Jonathan W.;Hendrickson, Bruce;Phillips, Cynthia A.
像万维网这样的巨型网络中的顶点社区很可能比网络本身小得多。然而，Rumanato和Barthelemy已经证明了用于社区检测的模块化最大化算法可能无法解决具有少于根L/2条边的社区，其中L是整个网络中的边数。这种分辨率限制导致模块化最大化算法在许多真实的网络上具有众所周知的低精度。Escherato和Barthelemy的论点也可以扩展到具有加权边的网络，并且我们导出了这个推论论点。我们的结论是，加权模块化算法可能无法解决社区小于根W是一个元素的/2总边的重量，其中W是在网络中的总边的重量，是一个元素的是一个社区间的边的最大重量。如果是一个小的元素，那么小社区可以解决。给定一个加权或未加权的网络，我们描述了如何获得新的边缘权重，以实现低的是一个元素，我们修改Clauset，纽曼，和摩尔（CNM）的社区检测算法，以最大限度地提高加权模块化，我们表明，由此产生的算法有很大的提高精度。在一个新兴的社区标准基准的实验中，我们发现我们简单的CNM变体与最准确的社区检测方法相比具有竞争力。
Communities of vertices within a giant network such as the World Wide Web are likely to be vastly smaller than the network itself. However, Fortunato and Barthelemy have proved that modularity maximization algorithms for community detection may fail to resolve communities with fewer than root L/2 edges, where L is the number of edges in the entire network. This resolution limit leads modularity maximization algorithms to have notoriously poor accuracy on many real networks. Fortunato and Barthelemy's argument can be extended to networks with weighted edges as well, and we derive this corollary argument. We conclude that weighted modularity algorithms may fail to resolve communities with less than root W is an element of/2 total edge weight, where W is the total edge weight in the network and is an element of is the maximum weight of an intercommunity edge. If is an element of is small, then small communities can be resolved. Given a weighted or unweighted network, we describe how to derive new edge weights in order to achieve a low is an element of, we modify the Clauset, Newman, and Moore (CNM) community detection algorithm to maximize weighted modularity, and we show that the resulting algorithm has greatly improved accuracy. In experiments with an emerging community standard benchmark, we find that our simple CNM variant is competitive with the most accurate community detection methods yet proposed.