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- Title
The Computational Complexity of Hierarchical Clustering Algorithms for Community Detection: A Review.
- Authors
Bui, Van Hieu; Phan, Huyen Trang
- Abstract
Community detection is a highly active research area that aims to identify groups of vertices with similar properties or interests within complex real-world networks. Over the years, a large number of papers have been published, resulting in the development of various community detection algorithms that consider factors such as the type of networks, the nature of communities, and applications. Despite numerous relevant review and survey papers, the literature lacks a comprehensive analysis of the computational complexity of existing community detection algorithms. This review aims to address this gap by providing a more detailed analysis and evaluation of the computational complexity of hierarchical clustering algorithms for community detection, including two main categories: agglomerative and divisive algorithms. We also highlight the main theoretical concepts, emphasizing the benefits and drawbacks of each approach, both in theory and in practical applications. This review helps researchers and practitioners in this field better understand valuable information on the differences and unique features of community detection algorithms with hierarchical community structure.
- Subjects
COMPUTATIONAL complexity; HIERARCHICAL clustering (Cluster analysis); COMPUTER algorithms; COMMUNITY organization; RANDOM walks
- Publication
Vietnam Journal of Computer Science (World Scientific), 2023, Vol 10, Issue 4, p409
- ISSN
2196-8888
- Publication type
Article
- DOI
10.1142/S2196888823300016