Version 1
: Received: 20 December 2020 / Approved: 21 December 2020 / Online: 21 December 2020 (10:44:19 CET)
How to cite:
Muscoloni, A.; Abdelhamid, I.; Decano, J. L.; Souza, E.; Maiorino, E.; Aikawa, M.; Silverman, E. K.; Sharma, A.; Cannistraci, C. V. Hyperedge Entanglement in High-Order Multilayer Networks. Preprints2020, 2020120500. https://doi.org/10.20944/preprints202012.0500.v1
Muscoloni, A.; Abdelhamid, I.; Decano, J. L.; Souza, E.; Maiorino, E.; Aikawa, M.; Silverman, E. K.; Sharma, A.; Cannistraci, C. V. Hyperedge Entanglement in High-Order Multilayer Networks. Preprints 2020, 2020120500. https://doi.org/10.20944/preprints202012.0500.v1
Muscoloni, A.; Abdelhamid, I.; Decano, J. L.; Souza, E.; Maiorino, E.; Aikawa, M.; Silverman, E. K.; Sharma, A.; Cannistraci, C. V. Hyperedge Entanglement in High-Order Multilayer Networks. Preprints2020, 2020120500. https://doi.org/10.20944/preprints202012.0500.v1
APA Style
Muscoloni, A., Abdelhamid, I., Decano, J. L., Souza, E., Maiorino, E., Aikawa, M., Silverman, E. K., Sharma, A., & Cannistraci, C. V. (2020). Hyperedge Entanglement in High-Order Multilayer Networks. Preprints. https://doi.org/10.20944/preprints202012.0500.v1
Chicago/Turabian Style
Muscoloni, A., Amitabh Sharma and Carlo Vittorio Cannistraci. 2020 "Hyperedge Entanglement in High-Order Multilayer Networks" Preprints. https://doi.org/10.20944/preprints202012.0500.v1
Abstract
Many real complex systems present multilayer structure where high-order metadata on one layer refers to dyadic data on a lower layer. Significant progresses to analyse high-order metadata under the assumption of community organization have been done. However, there are no planted communities in real-world networks, and the necessity of new frameworks to analyze high-order metadata regardless of community organization has been raised.Here, we propose to adopt hyperedge organization. Predicting ‘entanglements’ between a hyperedge and nodes scattered in the rest of the network might suggest structural or functional liaisons, without assumption of any community organization. We introduce a novel concept: hyperedge entanglement (HE), which associates to each hyperedge an entangled hyperedge, by means of a network operator that predicts significant ‘interactions at distance’ between network nodes and existing hyperedges. We also introduce a new challenge termed hyperedge entanglement prediction (HEP), and an algorithm to perform this task. We evaluated HEP performance on social, biological and synthetic data where, given only topology and hyperedges (such as communities or functional modules), the goal is to predict whether nodes not connected to a certain hyperedge might be candidates for a significant entanglement. Finally, as real application in diseasome systems biomedicine, we perform HEP on the human protein interactome to predict unknown gene entanglements with the COPD disease gene hyperedge. HEP predictions are validated by biological experiments, enlarging our understanding of molecular mechanisms behind COPD/aneurysm comorbidity.
Keywords
complex multilayer networks; hypergraphs; complex systems
Subject
Physical Sciences, Acoustics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.