Aoc

Assembling overlapping communities

Bibliographic Data

ID21256054
AuthorsAkhil Jakatdar (0000-0003-1580-010X, University of Illinois Urbana-Champaign), Baqiao Liu (0000-0002-4210-8269, University of Illinois Urbana-Champaign), Tandy Warnow (0000-0001-7717-3514, University of Illinois Urbana-Champaign), George Chacko (0000-0002-2127-1892, University of Illinois Urbana-Champaign)
Year2022
Volume3
Issue4
Pages1079-1096
Publication date2022-12-20
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueQuantitative Science Studies (JOURNAL)
Journal identifiersISSN: 2641-3337 • E-ISSN: 2641-3337
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/qss_a_00227
OpenAlexW4309694658
LanguageEN
References cited47

Through discovery of mesoscale structures, community detection methods contribute to the understanding of complex networks. Many community finding methods, however, rely on disjoint clustering techniques, in which node membership is restricted to one community or cluster. This strict requirement limits the ability to inclusively describe communities because some nodes may reasonably be assigned to multiple communities. We have previously reported Iterative K-core Clustering, a scalable and modular pipeline that discovers disjoint research communities from the scientific literature. We now present Assembling Overlapping Clusters (AOC), a complementary metamethod for overlapping communities, as an option that addresses the disjoint clustering problem. We present findings from the use of AOC on a network of over 13 million nodes that captures recent research in the very rapidly growing field of extracellular vesicles in biology

Biology · Computational biology · Anomaly Detection Techniques and Applications · Chemistry · Complex Network Analysis Techniques · Computer Science · Data Stream Mining Techniques

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