Abstract
An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence.
Highlights
Many complex systems in different areas such as sociology[1], biology[2], medicine[3], web[4] and computer science[5] can be represented as networks
We tested the performance of our algorithm with artificial networks and the real-world networks with the known community structure
The Dolphin Social Network describes the associations between 62 dolphins living in Doubtful Sound, New Zealand as reported by Lusseau[18]
Summary
Many complex systems in different areas such as sociology[1], biology[2], medicine[3], web[4] and computer science[5] can be represented as networks. Researchers have proposed several algorithms for detecting communities that optimize some local functions such as local modularity which require the knowledge of local network structure[11,12,13]. The main disadvantage of label propagation algorithms is that they produce no unique solutions They identify different partitions for the same network in multiple runs, while they use some dynamic information: the maximal number of neighbor labels that depends on the processing order of nodes and on some random chosen neighbor labels when there are more equal maximal neighbor labels. We investigate a fast and simple algorithm that uses the local network structure and requires neither optimization of pre-defined objective function nor information about number of communities and provides a unique community partition during multiple runs. To be local, it cannot be too restrictive, should not yield any cut-node or cut-link and is based on the meaningful definition of communities
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