Inferring meaningful communities from topology-constrained correlation networks.

ABSTRACT: Community structure detection is an important tool in graph analysis. This can be done, among other ways, by solving for the partition set which optimizes the modularity scores [Formula: see text]. Here it is shown that topological constraints in correlation graphs induce over-fragmentation of community structures. A refinement step to this optimization based on Linear Discriminant Analysis (LDA) and a statistical test for significance is proposed. In structured simulation constrained by topology, this novel approach performs better than the optimization of modularity alone. This method was also tested with two empirical datasets: the Roll-Call voting in the 110th US Senate constrained by geographic adjacency, and a biological dataset of 135 protein structures constrained by inter-residue

SUBMITTER: Hleap JS 

PROVIDER: S-EPMC4237410 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature