A Novel Clustering Algorithm Based Upon Games on Evolving Network
Qiang Li, Zhuo Chen, Yan He, Jing-ping Jiang

TL;DR
This paper presents a novel clustering approach using game theory on evolving networks, where data points adapt their connections based on payoffs, leading to automatic cluster formation through stable strategies.
Contribution
It introduces three new clustering algorithms based on game dynamics on evolving networks, linking cluster formation to evolutionarily stable strategies.
Findings
Clusters are formed automatically through stable strategies.
The algorithms effectively cluster datasets with reasonable accuracy.
Experimental results outperform some existing clustering methods.
Abstract
This paper introduces a model based upon games on an evolving network, and develops three clustering algorithms according to it. In the clustering algorithms, data points for clustering are regarded as players who can make decisions in games. On the network describing relationships among data points, an edge-removing-and-rewiring (ERR) function is employed to explore in a neighborhood of a data point, which removes edges connecting to neighbors with small payoffs, and creates new edges to neighbors with larger payoffs. As such, the connections among data points vary over time. During the evolution of network, some strategies are spread in the network. As a consequence, clusters are formed automatically, in which data points with the same evolutionarily stable strategy are collected as a cluster, so the number of evolutionarily stable strategies indicates the number of clusters.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
