2017/07/20 by Lu Wang, Wang, Lu, Zhengwu Zhang +3 · 1 citation
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Complex Network Analysis Techniques #Computation (stat.CO) #FOS: Computer and information sciences #Gene expression and cancer classification
paper · pdf · doi:10.48550/arxiv.1707.06360
openalex publication_date 2017/07/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This article focuses on the problem of studying shared- and individual-specific structure in replicated networks or graph-valued data. In particular, the observed data consist of n graphs, Gi, i=1,…,n, with each graph consisting of a collection of edges between V nodes. In brain connectomics, the graph for an individual corresponds to a set of interconnections among brain regions. Such data can be organized as a V × V binary adjacency matrix Ai for each i, with ones indicating an edge between a pair of nodes and zeros indicating no edge. When nodes have a shared meaning across replicates i=1,…,n, it becomes of substantial interest to study similarities and differences in the adjacency matrices. To address this problem, we propose a method to estimate a common structure and low-dimensional individual-specific deviations from replicated networks. The proposed Multiple GRAph Factorization (M-GRAF) model relies on a logistic regression mapping combined with a hierarchical eigenvalue decomposition. We develop an efficient algorithm for estimation and study basic properties of our approach. Simulation studies show excellent operating characteristics and we apply the method to human brain connectomics data.