hcga: Highly Comparative Graph Analysis for network phenotyping

Abstract

Networks are widely used as mathematical models of complex systems across many scientific disciplines, not only in biology and medicine but also in the social sciences, physics, computing and engineering. Decades of work have produced a vast corpus of research characterising the topological, combinatorial, statistical and spectral properties of graphs. Each graph property can be thought of as a feature that captures important (and some times overlapping) characteristics of a network. In the analysis of real-world graphs, it is crucial to integrate systematically a large number of diverse graph features in order to characterise and classify networks, as well as to aid network-based scientific discovery. In this paper, we introduce hcga, a framework for highly comparative analysis of graph data sets 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 characterisation of graph data sets. We show that hcga outperforms other methodologies on supervised classification tasks on benchmark data sets whilst retaining the interpretability of network features. We also illustrate how hcga can be used for network-based discovery through two examples where data is naturally represented as graphs: the clustering of a data set of images of neuronal morphologies, and a regression problem to predict charge transfer in organic semiconductors based on their structure. hcga is an open platform that can be expanded to include further graph properties and statistical learning tools to allow researchers to leverage the wide breadth of graph-theoretical research to quantitatively analyse and draw insights from network data.

Prediction of hospital-onset COVID-19 infections using dynamic networks of patient contact: an international retrospective cohort study

“…The underlying network structure might, therefore, hold features exploitable for HOCI prediction with network mining tools. 31 HOCIs were significantly more central in contact networks. Few studies have used contact data to investigate HCAI, and most have considered only direct contacts (ie, network degree).…”

Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study

“…Further, our model based only on contact network variables was found as predictive as a model including all other variables (Table 3; Figure 4). This indicates that the underlying network structure holds features that could be systematically exploited for HOCI prediction with network mining tools 32 . HOCIs were found to be highly central in contact networks with respect to both infected cases and overall patient connectivity.…”

Synchrony, oscillations, and phase relationships in collective neuronal activity: a highly comparative overview of methods

“…This matrix constitutes an empirical description of both sets of methods and time series, jointly considered, and its structure can be explored with both unsupervised and supervised techniques (Fulcher and Jones, 2014). While initially proposed for univariate time series analysis, the highly comparative approach has recently been extended to pairwise interactions in multivariate time series (Cliff et al, 2023) and graph-theoretic measures in networks (Peach et al, 2021). The results can recapitulate known theoretical relationships between methods using a data-driven approach, and can also offer unexpected insights into both the methods and the datasets being analyzed.…”