AOC: Assembling overlapping communities

Akhil Jakatdar, Baqiao Liu, Tandy Warnow, George Chacko

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)1079-1096
Number of pages18
JournalQuantitative Science Studies
Issue number4
StatePublished - Sep 1 2022
Externally publishedYes

ASJC Scopus subject areas

  • Analysis
  • Numerical Analysis
  • Cultural Studies
  • Library and Information Sciences


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