Fuzzy community detection via modularity guided membership-degree propagation
Fuzzy community detection via modularity guided membership-degree propagation
复制标题
通过模块化引导的隶属度传播进行模糊社区检测
DOI：
10.1016/j.patrec.2015.11.008
复制
发表时间：
2016-01
期刊：
影响因子：
--
通讯作者：
Bin Zhou
中科院分区：
文献类型：
--
作者：
Hengyuan Zhang;Xiaowu Chen;Jia Li;Bin Zhou
在复杂网络分析中，模糊社区检测是一个具有挑战性的任务，其目的是通过为各个社区分配每个顶点的定量隶属度来揭示网络结构。在本文中，我们提出了一种模糊社区检测方法，迭代传播的所有顶点的隶属度。在每一次迭代中，首先根据拓扑特征选择潜在社区的候选种子顶点。在此之后，在相邻的顶点之间传播的隶属度，使社区的数量可以得到关于所有选定的种子。为了确保模块化程度不断提高，在每次迭代中，我们都会丢弃降低社区分解模块化程度的选定种子。通过这种方式，可以充分利用网络的拓扑信息，社区沿着新种子的接受而逐渐涌现。在真实网络和合成网络上的实验结果表明，该方法具有令人印象深刻的性能，并且对不相交和模糊社区检测都具有鲁棒性。此外，所提出的方法表现出计算复杂度和整体性能之间的高度灵活性。
In complex network analysis, fuzzy community detection is a challenging task that aims to reveal the network structure by assigning each vertex quantitative membership-degrees to various communities. In this paper, we propose a fuzzy community detection method that iteratively propagates membership-degrees of all vertices. In each iteration, a candidate seed vertex of a potential community is first selected according to the topological characteristics. After that, the membership-degrees are propagated among adjacent vertices so that a number of communities can be obtained with respect to all selected seeds. To ensure that the modularity keeps improving, in each iteration we discard the selected seeds that decreases the modularity of the community decomposition. In this manner, the topological information about the network can be fully utilized, and communities gradually emerge along with the acceptance of new seeds. Experimental results on real-world and synthetic networks demonstrate that our approach has impressive performance and is robust on both disjoint and fuzzy community detections. Moreover, the proposed approach exhibits a high flexibility between computational complexity and overall performance.
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影响因子：
11.9
作者：
T. Havens;J. Bezdek;C. Leckie;K. Ramamohanarao;M. Palaniswami
通讯作者：
T. Havens;J. Bezdek;C. Leckie;K. Ramamohanarao;M. Palaniswami
影响因子：
4.6
作者：
Della Rossa, Fabio;Dercole, Fabio;Piccardi, Carlo
通讯作者：
Piccardi, Carlo
DOI：
10.1145/2339530.2339629
发表时间：
2012-08
期刊：
--
影响因子：
--
作者：
Yu Zhang;D. Yeung
通讯作者：
Yu Zhang;D. Yeung
基于局部随机游走和多维尺度的模糊重叠社区检测
DOI：
10.1016/j.physa.2013.08.028
发表时间：
2013-12
期刊：
Physica A: Statistical Mechanics and Its Applications
影响因子：
--
作者：
Wang, Wenjun;Liu, Dong;Liu, Xiao;Pan, Lin
通讯作者：
Pan, Lin
影响因子：
2.4
作者：
Raghavan, Usha Nandini;Albert, Reka;Kumara, Soundar
通讯作者：
Kumara, Soundar