Abstract
The neighborhood overlap (NOVER) of an edge u-v is defined as the ratio of the number of nodes who are neighbors for both u and v to that of the number of nodes who are neighbors of at least u or v. In this paper, we hypothesize that an edge u-v with a lower NOVER score bridges two or more sets of vertices, with very few edges (other than u-v) connecting vertices from one set to another set. Accordingly, we propose a greedy algorithm of iteratively removing the edges of a network in the increasing order of their neighborhood overlap and calculating the modularity score of the resulting network component(s) after the removal of each edge. The network component(s) that have the largest cumulative modularity score are identified as the different communities of the network. We evaluate the performance of the proposed NOVER-based community detection algorithm on nine real-world network graphs and compare the performance against the multi-level aggregation-based Louvain algorithm, as well as the original and time-efficient versions of the edge betweenness-based Girvan-Newman (GN) community detection algorithm.
Highlights
Community detection is one of the classical problems of complex network analysis
The cumulative modularity score of the communities obtained with our proposed Neighborhood OVERlap-based edge removal (NOVER) algorithm is observed to be only at most 60% lower than the cumulative modularity score obtained with the Girvan-Newman algorithm, whereas the time-complexity of neighborhood overlap (NOVER) is O(|E| ˆ (|E| + |V|)) and the actual execution time of the NOVER algorithm has been observed to be as small as just 1% of the execution time of the original Girvan-Newman algorithm on larger real-world networks
Though the Citation Graph Drawing (GD) Network (CN) network is a directed network, we modeled it as an undirected network for consistency with the other real-world networks considered as well as due to the underlying assumption of undirected edges for the community detection algorithms analyzed in this paper
Summary
Community detection is one of the classical problems of complex network analysis. A community in a network graph is a subset of the vertices that have a relatively larger density of edges among themselves than to the rest of the vertices [1]. Edge betweenness-based algorithms for community detection are typically considered to identify communities with a larger modularity score at the cost of a significantly larger computation time [5]. Another potential weakness of community detection algorithms could be the inability to detect smaller communities (called the resolution limit problem [6]); this weakness is more prominent in multi-level aggregation algorithms (like the Louvain algorithm [7]).
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