The paper presents an approach to mining heterogeneous information networks applied to a task of categorizing customers linked in a heterogeneous network of products, categories and customers. We propose a two step methodology to classify the customers. In the first step, the heterogeneous network is decomposed into several homogeneous networks using different connecting nodes. Similarly to the construction of bag-of-words vectors in text mining, we assign larger weights to more important nodes. In the second step, the resulting homogeneous networks are used to classify data either by network propositionalization or label propagation. Because the data set is highly imbalanced we adapt the label propagation algorithm to handle imbalanced data. We perform a series of experiments and compare different heuristics used in the first step of the methodology, as well as different classifiers which can be used in the second step of the methodology.
['Jan Kralj', 'Marko Robnik-Šikonja', 'Nada Lavraăź']
Heterogeneous Network Decomposition and Weighting with Text Mining Heuristics