A community integration strategy based on an improved modularity density increment for large-scale networks
A community integration strategy based on an improved modularity density increment for large-scale networks
复制标题
一种基于改进的大规模网络模块化密度增量的社区集成策略
DOI：
10.1016/j.physa.2016.11.066
复制
发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Yu Xue
中科院分区：
文献类型：
--
作者：
Ronghua Shang;Weitong Zhang;Licheng Jiao;Rustam Stolk;Yu Xue
提出了一种基于预划分的大规模网络社区集成策略，并对改进的模块密度增量Δ D进行了优化。我们所提出的方法首先搜索网络中的本地核心节点，即潜在的社区中心，并扩展这些社区，包括邻居节点具有足够高的相似性与核心节点。这样，我们可以有效地利用网络的节点和结构信息，准确地将网络预划分为社区。接下来，我们按照外部连接的降序排列这些预先划分的社区。这样，我们就可以确保在社区融合过程中优先考虑影响力较大的社区。同时，提出了一种改进的模块密度增量Δ D，并说明了如何将其作为社区集成优化过程中的目标函数。在社区合并过程中，优先合并那些外部连接较少的邻居社区，从而避免融合错误。最后，我们将全球推理的过程中，当地的整合。通过计算和比较每对群落的改进模块度密度增量，来判断是否进行群落整合，有效提高了群落整合的准确性。实验结果表明，我们提出的算法可以获得上级社区分类结果在5个大规模的网络，与其他8个著名的算法从文献中。
This paper presents a community integration strategy for large-scale networks, based on pre-partitioning, followed by optimization of an improved modularity density increment Δ D. Our proposed method initially searches for local core nodes in the network, ie potential community centers, and expands these communities to include neighbor nodes which have sufficiently high similarity with the core node. In this way, we can effectively exploit the information of the node and structure of the network, to accurately pre-partition the network into communities. Next, we arrange these pre-partitioned communities according to their external connections in descending order. In this way, we can ensure that communities with greater influence are prioritized during the process of community integration. At the same time, this paper proposes an improved modularity density increment Δ D, and shows how to use this as an objective function during the community integration optimization process. During the process of community consolidation, those neighbor communities with few external connections are prioritized for merging, thereby avoiding the fusion errors. Finally, we incorporate global reasoning into the process of local integration. We calculate and compare the improved modularity density increment of each pair of communities, to determine whether or not they should be integrated, effectively improve the accuracy of community integration. Experimental results show that our proposed algorithm can obtain superior community classification results on 5 large-scale networks, as compared with 8 other well known algorithms from the literature.
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DOI：
10.1103/physreve.76.011503
发表时间：
2007-07
期刊：
Physical review. E, Statistical, nonlinear, and soft matter physics
影响因子：
--
作者：
L. Bécu;Domitille Anache;S. Manneville;A. Colin
通讯作者：
L. Bécu;Domitille Anache;S. Manneville;A. Colin
DOI：
--
发表时间：
2004
期刊：
--
影响因子：
--
作者：
通讯作者：
--
影响因子：
2.4
作者：
Raghavan, Usha Nandini;Albert, Reka;Kumara, Soundar
通讯作者：
Kumara, Soundar
影响因子：
2.2
作者：
Shengdong Xie;Yuxiang Wang
通讯作者：
Shengdong Xie;Yuxiang Wang
DOI：
10.1088/1742-5468/2005/09/p09008
发表时间：
2005-09-01
影响因子：
2.4
作者：
Danon, L;Díaz-Guilera, A;Arenas, A
通讯作者：
Arenas, A