ArticleNature communications2017
Machine learning meets complex networks via coalescent embedding in the hyperbolic space.
Article in Nature communications, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Reward-enhanced encoding improves relearning of forgotten associations.Scientific reports · 2018Trial
- Hyperpathway: visualizing organization of pathway-molecule enriched interactions in omics studies via hyperbolic bipartite network embedding.NPJ systems biology and applications · 2026Article
- Interaction of Functional Brain Networks Is Associated With k-Clique Percolation in the Human Structural Connectome.Human brain mapping · 2025Article
- Geometric representations of brain networks can predict the surgery outcome in temporal lobe epilepsy.NPJ systems biology and applications · 2025Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Graphlet-based hyperbolic embeddings capture evolutionary dynamics in genetic networks.Bioinformatics (Oxford, England) · 2024Article
- Iterative embedding and reweighting of complex networks reveals community structure.Scientific reports · 2024Article
- Greedy routing optimisation in hyperbolic networks.Scientific reports · 2023Article
- The D-Mercator method for the multidimensional hyperbolic embedding of real networks.Nature communications · 2023Article
- Selection of Multi-Drug Targets against Drug-ResistantInternational journal of molecular sciences · 2023Article
- Brain network communication: concepts, models and applications.Nature reviews. Neuroscience · 2023Review
- Spatial Reconstruction of Oligo and Single Cells by De Novo Coalescent Embedding of Transcriptomic Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- Low-Complexity Hyperbolic Embedding Schemes for Temporal Complex Networks.Sensors (Basel, Switzerland) · 2022Article
- Geometrical congruence, greedy navigability and myopic transfer in complex networks and brain connectomes.Nature communications · 2022Article
- Detecting the ultra low dimensionality of real networks.Nature communications · 2022Article
- Joint Detection of Community and Structural Hole Spanner of Networks in Hyperbolic Space.Entropy (Basel, Switzerland) · 2022Article
- Generalised popularity-similarity optimisation model for growing hyperbolic networks beyond two dimensions.Scientific reports · 2022Article
- Progresses and challenges in link prediction.iScience · 2021Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physicists recently observed that realistic complex networks emerge as discrete samples from a continuous hyperbolic geometry enclosed in a circle: the radius represents the node centrality and the angular displacement between two nodes resembles their topological proximity. The hyperbolic circle aims to become a universal space of representation and analysis of many real networks. Yet, inferring the angular coordinates to map a real network back to its latent geometry remains a challenging inverse problem. Here, we show that intelligent machines for unsupervised recognition and visualization of similarities in big data can also infer the network angular coordinates of the hyperbolic model according to a geometrical organization that we term "angular coalescence." Based on this phenomenon, we propose a class of algorithms that offers fast and accurate "coalescent embedding" in the hyperbolic circle even for large networks. This computational solution to an inverse problem in physics of complex systems favors the application of network latent geometry techniques in disciplines dealing with big network data analysis including biology, medicine, and social science.
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