Linear Clustering Process on Networks

doi:10.1109/TNSE.2023.3271360

Linear Clustering Process on Networks

2023-01-01

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DOI：10.1109/TNSE.2023.3271360

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We propose a linear clustering process on a network consisting of two opposite forces: attraction and repulsion between adjacent nodes. Each node is mapped to a position on a one-dimensional line. The attraction and repulsion forces move the nodal position on the line, depending on how similar or different the neighbourhoods of two adjacent nodes are. Based on each node position, the number of clusters in a network and each node's cluster membership is estimated. The performance of the proposed linear clustering process is benchmarked on synthetic networks against widely accepted clustering algorithms such as modularity, Leiden method, Louvain method and the non-back tracking matrix. The proposed linear clustering process outperforms the most popular modularity-based methods, such as the Louvain method, on synthetic and real-world networks, while possessing a comparable computational complexity.

Keywords:

Clustering algorithms

Force

Benchmark testing

Partitioning algorithms

Heuristic algorithms

Mutual information

Matrix decomposition

Communities

graph clustering

modularity

linear process

Linear Clustering Process on Networks

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