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Block diagonal dominance-based dynamic programming for detecting community
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2020-01-16 , DOI: 10.1007/s11227-020-03151-y
Xingquan Li , Cong Cao , Tao Zhang

Clustering or partition is a fundamental work for graph or network. Detecting communities is a typical clustering, which divides a network into several parts according to the modularity. Community detection is a critical challenge for designing scalable, adaptive and survivable trust management protocol for a community of interest-based social IoT system. Most of the existed methods on community detection suffer from a common issue that the number of communities should be prior decided. This urges us to estimate the number of communities from the data by some way. This paper concurrently considers eliminating the number of communities and detecting communities based on block diagonal dominace adjacency matrix. To construct a block diagonal dominance adjacency matrix for the input network, it first reorders the node number by the breadth-first search algorithm. For the block diagonal dominance adjacency matrix, this paper shows that the numbers of nodes in a community should be continuous adjacent. And thus, it only needs insert some breakpoints in node number sequence to decide the number of communities and the nodes in every community. In addition, a dynamic programming algorithm is designed to achieve an optimal community detection result. Experimental results on a number of real-world networks show the effectiveness of the dynamic programming approach on the community detection problem.

中文翻译:

基于块对角优势的动态规划检测社区

聚类或分区是图或网络的基础工作。检测社区是一种典型的聚类,它根据模块性将网络分成几个部分。社区检测是为基于兴趣的社交物联网系统社区设计可扩展、自适应和可生存的信任管理协议的关键挑战。大多数现有的社区检测方法都存在一个共同的问题,即社区的数量应该事先决定。这促使我们通过某种方式从数据中估计社区的数量。本文同时考虑了基于块对角优势邻接矩阵消除社区数量和检测社区。为输入网络构造块对角优势邻接矩阵,它首先通过广度优先搜索算法对节点号重新排序。对于块对角优势邻接矩阵,本文表明社区中的节点数应该是连续相邻的。因此,只需要在节点编号序列中插入一些断点就可以决定社区的数量和每个社区中的节点。此外,还设计了动态规划算法以实现最优的社区检测结果。在许多真实世界网络上的实验结果表明了动态规划方法在社区检测问题上的有效性。它只需要在节点编号序列中插入一些断点来决定社区的数量和每个社区中的节点。此外,还设计了动态规划算法以实现最优的社区检测结果。在许多真实世界网络上的实验结果表明了动态规划方法在社区检测问题上的有效性。它只需要在节点编号序列中插入一些断点来决定社区的数量和每个社区中的节点。此外,还设计了动态规划算法以实现最优的社区检测结果。在许多真实世界网络上的实验结果表明了动态规划方法在社区检测问题上的有效性。
更新日期:2020-01-16
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