Comparison of modularity-based approaches for nodes clustering in hypergraphs
Statistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes' community in hypergraphs is not unique and encompasses various distinct situations. In this work, we conducted...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Peer Community In
2024-03-01
|
| Series: | Peer Community Journal |
| Subjects: | |
| Online Access: | https://peercommunityjournal.org/articles/10.24072/pcjournal.404/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1825206386929172480 |
|---|---|
| author | Poda, Veronica Matias, Catherine |
| author_facet | Poda, Veronica Matias, Catherine |
| author_sort | Poda, Veronica |
| collection | DOAJ |
| description | Statistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes' community in hypergraphs is not unique and encompasses various distinct situations. In this work, we conducted a comparative analysis of the performance of modularity-based methods for clustering nodes in binary hypergraphs. To address this, we begin by presenting, within a unified framework, the various hypergraph modularity criteria proposed in the literature, emphasizing their differences and respective focuses. Subsequently, we provide an overview of the state-of-the-art codes available to maximize hypergraph modularities for detecting node communities in hypergraphs. Through exploration of various simulation settings with controlled ground truth clustering, we offer a comparison of these methods using different quality measures, including true clustering recovery, running time, (local) maximization of the objective, and the number of clusters detected. Our contribution marks the first attempt to clarify the advantages and drawbacks of these newly available methods. This effort lays the foundation for a better understanding of the primary objectives of modularity-based node clustering methods for binary hypergraphs.
|
| format | Article |
| id | doaj-art-3c2a045732f840c293f2815bc4caead8 |
| institution | Kabale University |
| issn | 2804-3871 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Peer Community In |
| record_format | Article |
| series | Peer Community Journal |
| spelling | doaj-art-3c2a045732f840c293f2815bc4caead82025-02-07T10:17:18ZengPeer Community InPeer Community Journal2804-38712024-03-01410.24072/pcjournal.40410.24072/pcjournal.404Comparison of modularity-based approaches for nodes clustering in hypergraphs Poda, Veronica0Matias, Catherine1https://orcid.org/0000-0001-6665-2421University of Trento, Via Sommarive, 14, 38123, Povo, ItalyLaboratoire de Probabilités, Statistique et Modélisation, 4, Place Jussieu 75005 Paris, FranceStatistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes' community in hypergraphs is not unique and encompasses various distinct situations. In this work, we conducted a comparative analysis of the performance of modularity-based methods for clustering nodes in binary hypergraphs. To address this, we begin by presenting, within a unified framework, the various hypergraph modularity criteria proposed in the literature, emphasizing their differences and respective focuses. Subsequently, we provide an overview of the state-of-the-art codes available to maximize hypergraph modularities for detecting node communities in hypergraphs. Through exploration of various simulation settings with controlled ground truth clustering, we offer a comparison of these methods using different quality measures, including true clustering recovery, running time, (local) maximization of the objective, and the number of clusters detected. Our contribution marks the first attempt to clarify the advantages and drawbacks of these newly available methods. This effort lays the foundation for a better understanding of the primary objectives of modularity-based node clustering methods for binary hypergraphs. https://peercommunityjournal.org/articles/10.24072/pcjournal.404/Community detectionHigher-order interactionHypergraphModularityNode clustering |
| spellingShingle | Poda, Veronica Matias, Catherine Comparison of modularity-based approaches for nodes clustering in hypergraphs Peer Community Journal Community detection Higher-order interaction Hypergraph Modularity Node clustering |
| title | Comparison of modularity-based approaches for nodes clustering in hypergraphs
|
| title_full | Comparison of modularity-based approaches for nodes clustering in hypergraphs
|
| title_fullStr | Comparison of modularity-based approaches for nodes clustering in hypergraphs
|
| title_full_unstemmed | Comparison of modularity-based approaches for nodes clustering in hypergraphs
|
| title_short | Comparison of modularity-based approaches for nodes clustering in hypergraphs
|
| title_sort | comparison of modularity based approaches for nodes clustering in hypergraphs |
| topic | Community detection Higher-order interaction Hypergraph Modularity Node clustering |
| url | https://peercommunityjournal.org/articles/10.24072/pcjournal.404/ |
| work_keys_str_mv | AT podaveronica comparisonofmodularitybasedapproachesfornodesclusteringinhypergraphs AT matiascatherine comparisonofmodularitybasedapproachesfornodesclusteringinhypergraphs |