Knowl Inf Syst
https://doi.org/10.1007/s10115-018-1209-7
REGULAR PAPER
Fast detection of community structures using graph
traversal in social networks
Partha Basuchowdhuri
1
· Satyaki Sikdar
2
· Varsha Nagarajan
1
·
Khusbu Mishra
1
· Surabhi Gupta
1
· Subhashis Majumder
1
Received: 19 March 2016 / Revised: 26 December 2017 / Accepted: 6 May 2018
© Springer-Verlag London Ltd., part of Springer Nature 2018
Abstract Finding community structures in social networks is considered to be a challenging
task as many of the proposed algorithms are computationally expensive and does not scale
well for large graphs. Most of the community detection algorithms proposed till date are
unsuitable for applications that would require detection of communities in real time, espe-
cially for massive networks. The Louvain method, which uses modularity maximization to
detect clusters, is usually considered to be one of the fastest community detection algorithms
even without any provable bound on its running time. We propose a novel graph traversal-
based community detection framework, which not only runs faster than the Louvain method
but also generates clusters of better quality for most of the benchmark datasets. We show that
our algorithms run in O(|V |+|E|) time to create an initial cover before using modularity
maximization to get the final cover.
Keywords Community detection · Influenced Neighbor Score · Brokers · Community
nodes · Communities
1 Introduction
Networks can be realized by a graph data structure defined by G = (V , E),whereV denotes
the vertex set, E denotes the edge set and n =|V |, m =|E|. These networks exhibit
certain implicit characteristics like community structures. Communities in a network repre-
sent groups of vertices having dense intra-connections but sparse inter-connections. In this
paper, we use the terms cluster and community interchangeably. In a social network, a basic
Satyaki Sikdar: The work was done when the author was at Heritage Institute of Technology.
B
Partha Basuchowdhuri
parthabasu.chowdhuri@heritageit.edu
1
Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, WB,
India
2
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA
123
@Phil_Robichaud
@deepthiw
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