Finding community structure in very large networks
2004年12月6日 · Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with vertices and edges is where is the depth of the dendrogram describing the community structure.
Title: Finding community structure in very large networks
2004年8月9日 · Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure.
Finding community structure in very large networks - PubMed
The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. …
The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community, but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting
社区发现算法总结（一） - nolonely - 博客园
2017年1月8日 · 本算法的具体内容请参考 Finding and evaluating community structure in networks（Newman and Girvan） 。 重要概念. 边介数（betweenness）：网络中任意两个节点通过此边的最短路径的数目。 GN算法的思想：
Finding community structure in very large networks.
2004年8月9日 · This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability. Expand
Finding community structure in very large networks.[J]
Here we present a hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O(m d log n) where d is the depth of the dendrogram describing the community structure.
The discovery and analysis of community structure in networks is a topic of considerable recent interest within the physics community , but most methods proposed so far are unsuitable for very large networks because of their computational cost. Here we present a hierarchical agglomeration algorithm for detecting
Finding community structure in very large networks
We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation.
Finding community structure in very large networks
2005年1月1日 · In this study, we address these issues by proposing a novel network community structure similarity index (NCSSI) based on the edit distance concept.
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