Bounded Confidence Gossip Algorithms for Opinion Formation and Data Clustering

Linh Thi Hoai Nguyen, Takayuki Wada, Izumi Masubuchi, Toru Asai, Yasumasa Fujisaki

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


This paper presents a bounded confidence gossip algorithm for describing the process of opinion formation over a communication network. Each agent in the network keeps a time-varying opinion vector (or state), which represents its opinion about a set of matters. A common confidence threshold is set for all of the agents. The states of agents in the network will be updated time by time according to an iterative procedure: At each time, 1) one agent is chosen randomly, then it chooses one of its neighbors on the communication graph to contact with; 2) they exchange their states; and 3) if they have different states and the distance between their states is strictly smaller than the confidence threshold, they update their states as the average of the two. This algorithm converges almost surely to some equilibrium point such that any two adjacent agents either have the same state or have distinct states whose distance is no less than the confidence threshold. This is called the constant confidence threshold algorithm. An increasing confidence threshold algorithm, which repeats the constant confidence threshold algorithm several times with increasing confidence threshold, is also proposed. The algorithm is also convergent almost surely to some equilibrium point. Applicability of the method to clustering problems is shown through numerical examples.

Original languageEnglish
Article number8370736
Pages (from-to)1150-1155
Number of pages6
JournalIEEE Transactions on Automatic Control
Issue number3
Publication statusPublished - Mar 2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'Bounded Confidence Gossip Algorithms for Opinion Formation and Data Clustering'. Together they form a unique fingerprint.

Cite this