Empirical analysis of web-based user-object bipartite networks

TL;DR

This paper empirically analyzes the structure and clustering behavior of large-scale web-based user-object bipartite networks, revealing novel phenomena related to user selection mechanisms and their implications for information recommendation.

Contribution

It introduces a new collaborative clustering coefficient and provides detailed empirical insights into degree distributions and correlations in web-based bipartite networks.

Findings

Degree distributions follow specific patterns.

Novel collaborative clustering coefficient proposed.

Clustering properties relate to user selection mechanisms.

Abstract

Understanding the structure and evolution of web-based user-object networks is a significant task since they play a crucial role in e-commerce nowadays. This Letter reports the empirical analysis on two large-scale web sites, audioscrobbler.com and del.icio.us, where users are connected with music groups and bookmarks, respectively. The degree distributions and degree-degree correlations for both users and objects are reported. We propose a new index, named collaborative clustering coefficient, to quantify the clustering behavior based on the collaborative selection. Accordingly, the clustering properties and clustering-degree correlations are investigated. We report some novel phenomena well characterizing the selection mechanism of web users and outline the relevance of these phenomena to the information recommendation problem.