Estimating Network Parameters for Selecting Community Detection Algorithms

Abstract

This paper considers the problem of algorithm selection for community detection. The aim of community detection is to identify sets of nodes in a network which are more interconnected relative to their connectivity to the rest of the network. A large number of algorithms have been developed to tackle this problem, but as with any machine learning task there is no "one-size-fits-all" and each algorithm excels in a specific part of the problem space. This paper examines the performance of algorithms developed for weighted networks against those using unweighted networks for different parts of the problem space (parameterised by the intra/inter community links). It is then demonstrated how the choice of algorithm (weighted/unweighted) can be made based only on the observed network.

In proceedings of The 13th International Conference on Information Fusion

References in corpus (7)

• Maps of random walks on complex networks reveal community structure
• Near linear time algorithm to detect community structures in large-scale networks
• Detecting the overlapping and hierarchical community structure of complex networks
• Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities
• Clique percolation in random networks
• Finding Community Structure in Mega-scale Social Networks
• Community structures and role detection in music networks