ArticleNature communications2022
Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 13 citations in OpenAlex.
- Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding.NPJ systems biology and applications · 2026Article
- Greedy routing optimisation in hyperbolic networks.Scientific reports · 2023Article
- Improving Robustness of High-Low-Order Coupled Networks against Malicious Attacks Based on a Simulated Annealing Algorithm.Entropy (Basel, Switzerland) · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping.Nature communications · 2023Article
- Optimisation of the coalescent hyperbolic embedding of complex networks.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We introduce in network geometry a measure of geometrical congruence (GC) to evaluate the extent a network topology follows an underlying geometry. This requires finding all topological shortest-paths for each nonadjacent node pair in the network: a nontrivial computational task. Hence, we propose an optimized algorithm that reduces 26 years of worst scenario computation to one week parallel computing. Analysing artificial networks with patent geometry we discover that, different from current belief, hyperbolic networks do not show in general high GC and efficient greedy navigability (GN) with respect to the geodesics. The myopic transfer which rules GN works best only when degree-distribution power-law exponent is strictly close to two. Analysing real networks-whose geometry is often latent-GC overcomes GN as marker to differentiate phenotypical states in macroscale structural-MRI brain connectomes, suggesting connectomes might have a latent neurobiological geometry accounting for more information than the visible tridimensional Euclidean.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.