NetANOVA: novel graph clustering technique with significance assessment via hierarchical ANOVA.

ABSTRACT: Many problems in life sciences can be brought back to a comparison of graphs. Even though a multitude of such techniques exist, often, these assume prior knowledge about the partitioning or the number of clusters and fail to provide statistical significance of observed between-network heterogeneity. Addressing these issues, we developed an unsupervised workflow to identify groups of graphs from reliable network-based statistics. In particular, we first compute the similarity between networks via appropriate distance measures between graphs and use them in an unsupervised hierarchical algorithm to identify classes of similar networks. Then, to determine the optimal number of clusters, we recursively test for distances between two groups of networks. The test itself finds its inspiration in

SUBMITTER: Duroux D 

PROVIDER: S-EPMC10025436 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature