Search of Weighted Subgraphs on Complex Networks with Maximum Likelihood Methods

TL;DR

This paper introduces a maximum likelihood-based method for identifying weighted subgraphs in complex networks, demonstrating its effectiveness on both synthetic and real biological data.

Contribution

It develops a novel maximum likelihood approach for detecting weighted subgraphs, applicable to various network types and validated on real and simulated data.

Findings

Effective in identifying weighted subgraphs in synthetic networks

Successfully applied to yeast biological network data

Demonstrates efficiency on fully connected and real-world networks

Abstract

Real-data networks often appear to have strong modularity, or network-of-networks structure, in which subgraphs of various size and consistency occur. Finding the respective subgraph structure is of great importance, in particular for understanding the dynamics on these networks. Here we study modular networks using generalized method of maximum likelihood. We first demonstrate how the method works on computer-generated networks with the subgraphs of controlled connection strengths and clustering. We then implement the algorithm which is based on weights of links and show its efficiency in finding weighted subgraphs on fully connected graph and on real-data network of yeast.