Community detection on small random networks

Authors

Gabriel Dominik Sison National Institute of Physics, University of the Philippines Diliman
Giovanni Tapang National Institute of Physics, University of the Philippines Diliman

Abstract

A set of random graphs with 10, 20, and 30 nodes with 2 predefined communities were generated with varying probabilities of connection to determine the effect of network size on detection of communities using modularity. It was found that the highest modularity partitions found by the Girvan-Newman algorithm correspond to the most accurate partitions except when pin is less than 0.5 for 10 nodes and p_in less than 0.3 for 20 and 30 nodes. The accuracy of the algorithm is also lower when the number of nodes is 10 as compared to when there are 20 and 30 nodes.

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Issue

2011: Proceedings of the 29th Samahang Pisika ng Pilipinas National Physics Congress

Article ID

SPP2011-PA-14

Section

Poster Session PA

Published

2011-10-24

How to Cite

[1]

GD Sison and G Tapang, Community detection on small random networks, Proceedings of the Samahang Pisika ng Pilipinas 29, SPP2011-PA-14 (2011). URL: https://proceedings.spp-online.org/article/view/SPP2011-PA-14.