Learning an Integrated Distance Metric for Comparing Structure of Complex Networks

TL;DR

This paper introduces NetDistance, a learned integrated distance metric for complex networks that combines multiple structural features, improving network comparison accuracy over previous methods.

Contribution

The paper presents a novel distance metric learning approach, NetDistance, for comprehensive comparison of complex networks' structural properties.

Findings

NetDistance outperforms previous methods by at least 20% in precision.

The method effectively integrates multiple network features into a single metric.

Empirical evaluation demonstrates improved network classification accuracy.

Abstract

Graph comparison plays a major role in many network applications. We often need a similarity metric for comparing networks according to their structural properties. Various network features - such as degree distribution and clustering coefficient - provide measurements for comparing networks from different points of view, but a global and integrated distance metric is still missing. In this paper, we employ distance metric learning algorithms in order to construct an integrated distance metric for comparing structural properties of complex networks. According to natural witnesses of network similarities (such as network categories) the distance metric is learned by the means of a dataset of some labeled real networks. For evaluating our proposed method which is called NetDistance, we applied it as the distance metric in K-nearest-neighbors classification. Empirical results show that NetDistance outperforms previous methods, at least 20 percent, with respect to precision.