ArticleScientific reports2021
Detecting network anomalies using Forman-Ricci curvature and a case study for human brain networks.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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5 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder.Scientific reports · 2022Pooled it
- Cortical neuron classes and recursive curvature collapse: a neurobiological model of conscious dynamics.Theory in biosciences = Theorie in den Biowissenschaften · 2026Article
- Network Geometry of Borsa Istanbul: Analyzing Sectoral Dynamics with Forman-Ricci Curvature.Entropy (Basel, Switzerland) · 2025Article
- Temporal Anomaly Detection in Attention-Deficit/Hyperactivity Disorder Using Recurrent Neural Networks.Cureus · 2024Article
- Discrete Ricci curvatures capture age-related changes in human brain functional connectivity networks.Frontiers in aging neuroscience · 2023Article
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Authors and funding
5 authors at 2 institutions in 1 country.
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Abstract
We analyze networks of functional correlations between brain regions to identify changes in their structure caused by Attention Deficit Hyperactivity Disorder (ADHD). We express the task for finding changes as a network anomaly detection problem on temporal networks. We propose the use of a curvature measure based on the Forman-Ricci curvature, which expresses higher-order correlations among two connected nodes. Our theoretical result on comparing this Forman-Ricci curvature with another well-known notion of network curvature, namely the Ollivier-Ricci curvature, lends further justification to the assertions that these two notions of network curvatures are not well correlated and therefore one of these curvature measures cannot be used as an universal substitute for the other measure. Our experimental results indicate nine critical edges whose curvature differs dramatically in brains of ADHD patients compared to healthy brains. The importance of these edges is supported by existing neuroscience evidence. We demonstrate that comparative analysis of curvature identifies changes that more traditional approaches, for example analysis of edge weights, would not be able to identify.
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