Weighted network modules
Weighted network modules
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DOI：
10.1088/1367-2630/9/6/180
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发表时间：
2007-06-28
影响因子：
3.3
通讯作者：
Vicsek, Tamas
中科院分区：
文献类型：
--
作者：
Farkas, Illes J.;Abel, Daniel;Vicsek, Tamas
将链接权重包含到网络属性分析中，可以更深入地了解现实世界网络的模块化结构（通常是重叠的）。基于足够高强度k-团的渗透概念，提出了一种加权网络的带权团渗透方法（CPMw）聚类算法。该算法允许模块之间的重叠。首先，我们给出了（加权）Erdos-Renyi图上加权k团渗流临界点的详细解析和数值结果。然后，对一个科学家合作网络和一个股票相关图，我们计算了三个链接的权重相关性，并用CPMw计算了加权模块。在对两个网络中的链路权重进行重新洗牌并对随机控制图计算相同的数量之后，我们表明三个或更多强链路的组在两个原始图中更倾向于聚类在一起。
The inclusion of link weights into the analysis of network properties allows a deeper insight into the (often overlapping) modular structure of real-world webs. We introduce a clustering algorithm clique percolation method with weights (CPMw) for weighted networks based on the concept of percolating k-cliques with high enough intensity. The algorithm allows overlaps between the modules. First, we give detailed analytical and numerical results about the critical point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then, for a scientist collaboration web and a stock correlation graph we compute three-link weight correlations and with the CPMw the weighted modules. After reshuffling link weights in both networks and computing the same quantities for the randomized control graphs as well, we show that groups of three or more strong links prefer to cluster together in both original graphs.