Non-parametric resampling of random walks for spectral network clustering
arXiv:1304.4156 · doi:10.1103/PhysRevE.89.012802
Abstract
Parametric resampling schemes have been recently introduced in complex network analysis with the aim of assessing the statistical significance of graph clustering and the robustness of community partitions. We propose here a method to replicate structural features of complex networks based on the non-parametric resampling of the transition matrix associated with an unbiased random walk on the graph. We test this bootstrapping technique on synthetic and real-world modular networks and we show that the ensemble of replicates obtained through resampling can be used to improve the performance of standard spectral algorithms for community detection.
5 pages, 2 figures
References in corpus (13)
- Modularity and community structure in networks
- Community detection in graphs
- Cooperative Game Theory Approaches for Network Partitioning
- Benchmark graphs for testing community detection algorithms
- Consensus clustering in complex networks
- Spectral redemption: clustering sparse networks
- Robustness of community structure in networks
- Spectral coarse-graining of complex networks
- Spectral and Dynamical Properties in Classes of Sparse Networks with Mesoscopic Inhomogeneities
- Detecting synchronization clusters in multivariate time series via coarse-graining of Markov chains
- Networks of motifs from sequences of symbols
- Systematic identification of statistically significant network measures
- Resampling effects on significance analysis of network clustering and ranking