HCGA: Highly comparative graph analysis for network phenotyping.

ABSTRACT: Networks are widely used as mathematical models of complex systems across many scientific disciplines. Decades of work have produced a vast corpus of research characterizing the topological, combinatorial, statistical, and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and sometimes overlapping) characteristics of a network. In this paper, we introduce HCGA, a framework for highly comparative analysis of graph datasets that computes several thousands of graph features from any given network. HCGA also offers a suite of statistical learning and data analysis tools for automated identification and selection of important and interpretable features underpinning the characterization of graph datasets. We show that HCGA outperforms othe

SUBMITTER: Peach RL 

PROVIDER: S-EPMC8085611 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature