30 January 2024 to 27 February 2024
University of Houston - Main Campus
US/Central timezone

Finding Community Structure in Networks with RenEEL and its Extension to Bipartite Networks

24 Feb 2024, 13:24
12m
University of Houston - Main Campus

University of Houston - Main Campus

101 Farish Hall
Talk Biological and Statistical Physics Statistical Physics and Condensed Matter and Others

Speaker

Ms TANIA GHOSH (University of Houston)

Description

Arguably the most fundamental problem in Network Science is finding structure within a complex network. One approach is to partition the nodes into communities that are more densely connected than one expects in a random network. “The” community structure corresponds to the partition that maximizes a measure that quantifies this idea. Finding the maximizing partition, however, is a computationally difficult NP-complete problem. We explore the use of a recently introduced algorithmic scheme [Guo, Singh, and Bassler, Sci. Rep. 9, 14234 (2019)] to find the structure of a set of benchmark networks. The scheme, known as Reduced Network Extremal Ensemble Learning (RenEEL), creates an ensemble of $k$ partitions and updates the ensemble by replacing its worst member with the best of $k’$ partitions found by analyzing a simplified network. The updating continues until consensus is achieved within the ensemble. Varying the values of $k$ and $k’$, we find that the results obey different classes of extreme value statistics and that increasing $k$ is generally much more effective than increasing $k’$ for finding the best partition. Building upon this exploration, we propose to extend the methodology addressed in [Guo, Singh, and Bassler, J. Phys. Complex. 4 (2023) 025001] to bipartite networks. Introducing a novel metric, bipartite generalized modularity density $Q_{bg}$. This function has a tunable parameter that sets the scale for the typical community found. By varying this parameter hierarchical structure can be found in bipartite networks.

Academic year 4th year
Research Advisor Dr. Gemunu Gunaratne, Dr. Kevin E Bassler

Author

Ms TANIA GHOSH (University of Houston)

Co-authors

Dr Kevin Bassler (University of Houston) Dr R. K. P. Zia (University of Houston)

Presentation materials