Learning an Integrated Distance Metric for Comparing Structure of Complex Networks
This addresses the need for a global similarity metric in network applications, but it is incremental as it builds on existing distance metric learning techniques.
The paper tackles the problem of comparing complex networks by developing an integrated distance metric called NetDistance, which learns from labeled network data to capture structural properties, and shows it outperforms previous methods by at least 20% in precision.
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.