Comparison of modularity-based approaches for nodes clustering in hypergraphsuse asterix (*) to get italics

Veronica Poda, Catherine MatiasPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"

2024

<p>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.</p> <p>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 binary 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.&nbsp;</p>

https://github.com/veronicapoda/modularity/You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https://

https://github.com/veronicapoda/modularity/You should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://

community detection; higher-order interaction; hypergraph; modularity; node clustering

NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).

Clustering in networks, Graph algorithms

Phil Chodrow, pchodrow@middlebury.edu, Nate Veldt, nveldt@tamu.edu, Austin R. Benson, arb@cs.cornell.edu, Bogumił Kamiński, bogumil.kaminski@sgh.waw.pl, Paweł Prałat, pralat@torontomu.ca, François Théberge, theberge@ieee.org, Balaraman Ravindran, ravi@cse.iitm.ac.in

e.g. John Doe john@doe.com

No need for them to be recommenders of PCI Network Sci. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct

2024-01-25 10:19:55

Remy Cazabet