In:
Modern Physics Letters B, World Scientific Pub Co Pte Ltd, Vol. 30, No. 13 ( 2016-05-20), p. 1650187-
Abstract:
The study of community structure is an important problem in a wide range of applications, which can help us understand the real network system deeply. However, due to the existence of random factors and error edges in real networks, how to measure the significance of community structure efficiently is a crucial question. In this paper, we present a novel statistical framework computing the significance of community structure across multiple optimization methods. Different from the universal approaches, we calculate the similarity between a given node and its leader and employ the distribution of link tightness to derive the significance score, instead of a direct comparison to a randomized model. Based on the distribution of community tightness, a new “[Formula: see text]-value” form significance measure is proposed for community structure analysis. Specially, the well-known approaches and their corresponding quality functions are unified to a novel general formulation, which facilitates in providing a detailed comparison across them. To determine the position of leaders and their corresponding followers, an efficient algorithm is proposed based on the spectral theory. Finally, we apply the significance analysis to some famous benchmark networks and the good performance verified the effectiveness and efficiency of our framework.
Type of Medium:
Online Resource
ISSN:
0217-9849
,
1793-6640
DOI:
10.1142/S0217984916501876
Language:
English
Publisher:
World Scientific Pub Co Pte Ltd
Publication Date:
2016
detail.hit.zdb_id:
896450-6
detail.hit.zdb_id:
2038670-9
