Hypergraph animals DOI
Michael P. H. Stumpf

Physical review. E, Journal Year: 2024, Volume and Issue: 110(4)

Published: Oct. 17, 2024

Here we introduce simple structures for the analysis of complex hypergraphs, hypergraph animals. These are designed to describe local node neighborhoods nodes in hypergraphs. We establish their relationships lattice animals and network motifs, develop combinatorial properties sparse uncorrelated make use tight link partition numbers, which opens up a vast mathematical framework then study abundances random Two transferable insights result from this analysis: (i) it establishes importance high-cardinality edges ensembles hypergraphs that inspired by classical Erdös-Renyí graphs; (ii) there is close connection between degree hyperedge cardinality shapes animal spectra profoundly. Both findings imply can have potential affect information flow processing systems. Our also suggests need spend more effort on investigating developing suitable conditional capture real-world dependency structures.

Language: Английский

Hypergraph Motif Representation Learning DOI
Alessia Antelmi, Gennaro Cordasco, Daniele De Vinco

et al.

Published: April 4, 2025

Language: Английский

Citations

0

A powerful lens for temporal network analysis: temporal motifs DOI Creative Commons
Ahmet Erdem Sarıyüce

Discover Data, Journal Year: 2025, Volume and Issue: 3(1)

Published: April 28, 2025

Language: Английский

Citations

0

Multiplex measures for higher-order networks DOI Creative Commons
Quintino Francesco Lotito, Alberto Montresor, Federico Battiston

et al.

Applied Network Science, Journal Year: 2024, Volume and Issue: 9(1)

Published: Sept. 3, 2024

Abstract A wide variety of complex systems are characterized by interactions different types involving varying numbers units. Multiplex hypergraphs serve as a tool to describe such structures, capturing distinct higher-order among collection In this work, we introduce comprehensive set measures structural connectivity patterns in multiplex hypergraphs, considering scales from node and hyperedge levels the system’s mesoscale. We validate our with three real-world datasets: scientific co-authorship physics, movie collaborations, high school interactions. This validation reveals new collaboration patterns, identifies trends within across subfields, provides insights into daily interaction dynamics. Our framework aims offer more nuanced characterization marked both

Language: Английский

Citations

2

A unified active learning framework for annotating graph data for regression task DOI Creative Commons
Peter Samoaa,

Linus Aronsson,

Antonio Longa

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 138, P. 109383 - 109383

Published: Oct. 4, 2024

Language: Английский

Citations

1

Social Inclusion of Gen Z Ukrainian Refugees in Lithuania: The Role of Online Social Networks DOI Creative Commons
Isabel Palomo-Domínguez, Jolanta Pivorienė, Odeta Merfeldaitė

et al.

Social Sciences, Journal Year: 2024, Volume and Issue: 13(7), P. 361 - 361

Published: July 5, 2024

Since the start of war in Ukraine, Lithuania, a country barely 3 million inhabitants, has welcomed more than 85,000 refugees, mainly minors and young people. This research focuses on youth segment, members Gen Z, which exhibits marked gender bias, as majority are women. The purpose this study is to determine role played by online social networks process inclusion host community. Methodologically, conducts qualitative approach through in-depth interviews with open code content analysis. results point changes their behavior media users, such using new networks, greater attention practical topics knowing necessary services leisure opportunities environment. Among conclusions, positive effect these refugees stands out: being local virtual community facilitates interactions physical world country.

Language: Английский

Citations

0

Hypergraph animals DOI
Michael P. H. Stumpf

Physical review. E, Journal Year: 2024, Volume and Issue: 110(4)

Published: Oct. 17, 2024

Here we introduce simple structures for the analysis of complex hypergraphs, hypergraph animals. These are designed to describe local node neighborhoods nodes in hypergraphs. We establish their relationships lattice animals and network motifs, develop combinatorial properties sparse uncorrelated make use tight link partition numbers, which opens up a vast mathematical framework then study abundances random Two transferable insights result from this analysis: (i) it establishes importance high-cardinality edges ensembles hypergraphs that inspired by classical Erdös-Renyí graphs; (ii) there is close connection between degree hyperedge cardinality shapes animal spectra profoundly. Both findings imply can have potential affect information flow processing systems. Our also suggests need spend more effort on investigating developing suitable conditional capture real-world dependency structures.

Language: Английский

Citations

0