The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
Community detection based on a semantic network
Knowledge-Based Systems
Topic oriented community detection through social objects and link analysis in social networks
Knowledge-Based Systems
A novel measure of edge centrality in social networks
Knowledge-Based Systems
Community detection by using the extended modularity
Acta Cybernetica
Identifying influential nodes in complex networks with community structure
Knowledge-Based Systems
An efficient algorithm for community mining with overlap in social networks
Expert Systems with Applications: An International Journal
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In the study of complex networks, a network is said to have community structure if it divides naturally into groups of nodes with dense connections within groups and only sparser connections between them [1]. Community structures are quite common in real networks. Social networks often include community groups based on common location, interests, occupation, etc. One of the most widely used methods for community detection is modularity maximization [2]. Modularity is a function that measures the quality of a particular division of a network into communities. But in [3], it is shown that communities that maximize the modularity are certainly groupings of smaller communities that need to be studied. In this work, we define a new function that qualifies a partition. We also present an algorithm that optimizes this function in order to find, within a reasonable time, the partition with the best measure of quality and which does not ignore small community.