Role model detection using low rank similarity matrix

TL;DR

This paper introduces a new low-rank similarity matrix method for role detection in large networks, improving computational efficiency and role extraction accuracy compared to existing methods.

Contribution

The paper presents a novel non-recursive similarity measure and a linear time complexity algorithm for role detection in large networks.

Findings

The new similarity measure outperforms Browet's measure in role extraction.

The algorithm achieves linear time complexity, enabling analysis of large networks.

Experimental results validate the effectiveness and scalability of the proposed method.

Abstract

Computing meaningful clusters of nodes is crucial to analyse large networks. In this paper, we apply new clustering methods to improve the computational time. We use the properties of the adjacency matrix to obtain better role extraction. We also define a new non-recursive similarity measure and compare its results with the ones obtained with Browet's similarity measure. We will show the extraction of the different roles with a linear time complexity. Finally, we test our algorithm with real data structures and analyse the limit of our algorithm.