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5 Summary

Fig. 2. Accuracies when turning w1 : w2

We introduced a generalization of random walk graph kernel from the setting of homogeneous networks, i.e., networks consisting of only one type of nodes and one type of links, to the setting of heterogeneous networks, i.e., networks consisting of multiple types of nodes and links. We used the resulting kernel, HGK, to train an SVM classifier for labeling actors in heterogeneous social networks. The results of our experiments show that HGK classifiers often significantly outperform or are competitive with the state-of-the-art methods for labeling actors in social networks. Some promising directions for further research include: (i) combining multiple kernels [18] that capture different notions of similarity between nodes in heterogeneous networks; and (ii) using linking preferences directly estimated from the data to improve the accuracy of predicted labels.

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