On consensus-based community detection

Research output: Chapter in Book/Entry/PoemConference contribution


We consider networks in which every node updates its value in discrete time by taking a weighted average of the values of the nodes it interacts with. Using an objective function that quantifies the efficiency with which clusters of interacting nodes converge to consensus internally, we formulate an optimization problem that identifies distinct communities in the network. The optimal community detection problem is combinatorial in nature and intractable in general, and we use convex relaxations to reformulate the problem as a semidefinite program. We demonstrate the utility of our algorithm by applying it to some benchmark graphs from the network science literature.

Original languageEnglish (US)
Title of host publication54rd IEEE Conference on Decision and Control,CDC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781479978861
StatePublished - Feb 8 2015
Event54th IEEE Conference on Decision and Control, CDC 2015 - Osaka, Japan
Duration: Dec 15 2015Dec 18 2015

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume54rd IEEE Conference on Decision and Control,CDC 2015
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Other54th IEEE Conference on Decision and Control, CDC 2015


  • Clustering
  • community detection
  • consensus
  • convex relaxation
  • dynamical systems
  • graph partitioning
  • optimization
  • semidefinite programming
  • social networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


Dive into the research topics of 'On consensus-based community detection'. Together they form a unique fingerprint.

Cite this