arXiv subjects

Jian-Ming Li

Publications and source records attributed to Jian-Ming Li.

4 recordsLinked to original sources

arXiv2018

Identifying multi-scale communities in networks by asymptotic surprise

Optimizing statistical measures for community structure is one of the most popular strategies for community detection, but many of them lack the flexibility of resolution and thus are incompatible with multi-scale communities of networks. Here, we further studied a statistical measure of interest for community detection, asymptotic surprise, an asymptotic approximation of surprise. We discussed the critical behaviors of asymptotic surprise in phase transition of community partition theoretically. Then, according to the theoretical analysis, a multi-resolution method based on asymptotic surprise was introduced, which provides an alternative approach to study multi-scale networks, and an improved Louvain algorithm was proposed to optimize the asymptotic surprise more effectively. By a series of experimental tests in various networks, we validated the critical behaviors of the asymptotic surprise further and the effectiveness of the improved Louvain algorithm, displayed its ability to solve the first-type resolution limit and stronger tolerance against the second-type resolution limit, and confirmed its effectiveness of revealing multi-scale community structures in multi-scale networks.

physics.soc-ph↗

arXiv2017

Community detection based on significance optimization in complex networks

Community structure is an important structural property that extensively exists in various complex networks. In the past decade, much attention has been paid to the design of community-detection methods, but analyzing the behaviors of the methods is also of interest in the theoretical research and real applications. Here, we focus on an important measure for community structure, significance [Sci. Rep. 3 (2013) 2930]. Specifically, we study the effect of various network parameters on this measure in detail, analyze the critical behaviors of it in partition transition, and analytically give the formula of the critical points and the phase diagrams. The results shows that the critical number of communities in partition transition increases dramatically with the difference between inter- and intra-community link densities, and thus significance optimization displays higher resolution in community detection than many other methods, but it is also easily to lead to the excessive splitting of communities. By Louvain algorithm for significance optimization, we confirmed the theoretical results on artificial and real-world networks, and give a series of comparisons with some classical methods.

physics.soc-ph↗

arXiv2016

Enhancing community detection by local structural information

Many real-world networks such as the gene networks, protein-protein interaction networks and metabolic networks exhibit community structures, meaning the existence of groups of densely connected vertices in the networks. Many local similarity measures in the networks are closely related to the concept of the community structures, and may have positive effect on community detection in the networks. Here, various local similarity measures are used to extract the local structural information and then are applied to community detection in the networks by using the edge-reweighting strategy. The effect of the local similarity measures on community detection is carefully investigated and compared in various networks. The experimental results show that the local similarity measures are crucial to the improvement for the community detection methods, while the positive effect of the local similarity measures is closely related to the networks under study and the applied community detection methods.

physics.soc-ph↗

arXiv2015

Analysis and perturbation of degree correlation in complex networks

Degree correlation is an important topological property common to many real-world networks. In this paper, the statistical measures for characterizing the degree correlation in networks are investigated analytically. We give an exact proof of the consistency for the statistical measures, reveal the general linear relation in the degree correlation, which provide a simple and interesting perspective on the analysis of the degree correlation in complex networks. By using the general linear analysis, we investigate the perturbation of the degree correlation in complex networks caused by the addition of few nodes and the rich club. The results show that the assortativity of homogeneous networks such as the ER graphs is easily to be affected strongly by the simple structural changes, while it has only slight variation for heterogeneous networks with broad degree distribution such as the scale-free networks. Clearly, the homogeneous networks are more sensitive for the perturbation than the heterogeneous networks.

physics.soc-ph↗