HCGA: Highly comparative graph analysis for network phenotyping.

Autor:	Peach RL; Department of Mathematics, Imperial College London, SW7 2AZ London, UK., Arnaudon A; Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, 1202 Geneva, Switzerland., Schmidt JA; Department of Chemistry, Imperial College London, SW7 2AZ London, UK., Palasciano HA; Department of Mathematics, Imperial College London, SW7 2AZ London, UK., Bernier NR; Miraex, EPFL Innovation Park, 1024 Ecublens, Switzerland., Jelfs KE; Department of Chemistry, Imperial College London, SW7 2AZ London, UK., Yaliraki SN; Department of Chemistry, Imperial College London, SW7 2AZ London, UK., Barahona M; Department of Mathematics, Imperial College London, SW7 2AZ London, UK.
Jazyk:	angličtina
Zdroj:	Patterns (New York, N.Y.) [Patterns (N Y)] 2021 Apr 02; Vol. 2 (4), pp. 100227. Date of Electronic Publication: 2021 Apr 02 (Print Publication: 2021).
DOI:	10.1016/j.patter.2021.100227
Abstrakt:	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 other methodologies on supervised classification tasks on benchmark datasets while retaining the interpretability of network features. We exemplify HCGA by predicting the charge transfer in organic semiconductors and clustering a dataset of neuronal morphology images. Competing Interests: The authors declare no competing interests. (© 2021 The Authors.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu