Learning an Integrated Distance Metric for Comparing Structure of Complex Networks [article]

Sadegh Aliakbary, Sadegh Motallebi, Jafar Habibi, Ali Movaghar
2013 arXiv   pre-print
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
more » ... or 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.
arXiv:1307.3626v1 fatcat:a4koykbi2jcnrg2uyrbu6vprou