Structure-Preserving Sparsification of Social Networks

TL;DR

This paper systematically compares edge sparsification methods on social networks, introduces a new Local Degree method, and demonstrates that many network properties can be preserved with significant edge reduction.

Contribution

It provides the first comprehensive comparison of sparsification techniques, proposes a novel Local Degree method, and evaluates their effectiveness on large-scale social networks.

Findings

Many properties preserved at 20% of edges

Local Degree method is fast and effective

Different methods suit different network properties

Abstract

Sparsification reduces the size of networks while preserving structural and statistical properties of interest. Various sparsifying algorithms have been proposed in different contexts. We contribute the first systematic conceptual and experimental comparison of \textit{edge sparsification} methods on a diverse set of network properties. It is shown that they can be understood as methods for rating edges by importance and then filtering globally by these scores. In addition, we propose a new sparsification method (\textit{Local Degree}) which preserves edges leading to local hub nodes. All methods are evaluated on a set of 100 Facebook social networks with respect to network properties including diameter, connected components, community structure, and multiple node centrality measures. Experiments with our implementations of the sparsification methods (using the open-source network analysis tool suite NetworKit) show that many network properties can be preserved down to about 20\% of the original set of edges. Furthermore, the experimental results allow us to differentiate the behavior of different methods and show which method is suitable with respect to which property. Our Local Degree method is fast enough for large-scale networks and performs well across a wider range of properties than previously proposed methods.