Using tf-idf as an edge weighting scheme in user-object bipartite networks

TL;DR

This paper introduces a tf-idf based edge weighting method for user-object bipartite networks, improving community detection and network density by addressing edge inflation caused by popular objects.

Contribution

It proposes a novel tf-idf based edge weighting scheme that enhances the analysis of user-object bipartite networks, addressing issues of edge inflation and community quality.

Findings

Improved community structure in projected networks

Reduced edge inflation in user networks

Enhanced network density and quality

Abstract

Bipartite user-object networks are becoming increasingly popular in representing user interaction data in a web or e-commerce environment. They have certain characteristics and challenges that differentiates them from other bipartite networks. This paper analyzes the properties of five real world user-object networks. In all cases we found a heavy tail object degree distribution with popular objects connecting together a large part of the users causing significant edge inflation in the projected users network. We propose a novel edge weighting strategy based on tf-idf and show that the new scheme improves both the density and the quality of the community structure in the projections. The improvement is also noticed when comparing to partially random networks.