Affiliations: [a] Department of Computer Science, South Asian University, Delhi, India
| [b] Department of Information Technology, Central University of Kashmir, J&K, India
| [c] Department of Computer Sciences, University of Kashmir, J&K, India
Correspondence:
[*]
Corresponding author. Sajid Yousuf Bhat, Department of Computer Sciences, University of Kashmir, J&K, India. E-mail: [email protected].
Note: [1] For undirected networks, each edge is represented by two records with swapped source and destination fields and may have different weights.
Abstract: Community detection from networks is one of the long standing and challenging tasks in the field of complex network research. Detection of communities poses numerous challenges in terms of their overlapping and hierarchical nature, dynamics of networks and underlying communities, scalability of detection algorithms on large scale networks to mention a few. Traditional community detection methods are not readily scalable to large networks mainly due to the computation of global network metrics. This paper presents a novel scalable overlapping community detection approach for large scale networks by presenting a MapReduce framework based implementation of a density-based local community detection method. The method is divided in two stages where the first stage uses a MapReduce approach to identify a mutual-core connected subgraph of the underlying network. The second stage uses an existing connected component detection method, implemented via MapReduce, to identify connected components in the mutual-core connected subgraph generated in the first stage. A community is then taken as the union of the core-nodes in a connected component and the respective density-based neighborhood of each core-node in the connected component. The resulting approach is among the first scalable overlapping community detection methods proposed in literature.
Keywords: Community detection, overlapping community, connected components, mapreduce, large-scale networks
