A Distributed Higher-Order [Formula Omitted]-Medoids Clustering Algorithm for Network Partition

• Published in: IEEE transactions on network science and engineering Vol. 11; no. 5; p. 4181
• Main Authors: Mo, Yuanqiu, Xing, Ran, Hou, Huazhou
• Format: Journal Article
• Language: English
• Published: Piscataway The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01-01-2024
• Subjects: Algorithms; Clustering; Nodes; Shortest-path problems; Social networks; Wireless sensor networks
• ISSN: 2334-329X
• DOI: 10.1109/TNSE.2024.3402383

In this paper, we develop a distributed higher-order k -medoids clustering algorithm for networks using hop count as the distance metric, typical examples include social networks, wireless sensor networks and etc. Different than the classical k -medoids clustering where each cluster is assigned with one medoid (representative nodes in the network), the proposed algorithm is capable of partitioning the nodes into clusters dominated by multiple medoids. In this algorithm, a higher-order shortest path algorithm is first adopted to assign each node the clustering set comprising of its h ([Formula Omitted]) closest medoids using the hop count metric, then an aggregation algorithm collects the local information from each node to the medoids in its clustering set, based on which a medoids update mechanism helps each medoid decide whether to keep its medoid status or assign the status to one of its neighbors, so as to improve the clustering quality. The clustering performance of the proposed algorithm is proved to monotonically converge to a local optimum after running the algorithm finite times. Simulations results show that the proposed algorithm can achieve superior performance compared with several centralized [Formula Omitted]-medoids clustering algorithms and scale well as the number of nodes increases.