Scalable SVM-Based Classification in Dynamic Graphs

With the emergence of networked data, graph classification has received considerable interest during the past years. Most approaches to graph classification focus on designing effective kernels to compute similarities for static graphs. However, they become computationally intractable in terms of time and space when a graph is presented in a incremental fashion with continuous updates, i.e., Insertions of nodes and edges. In this paper, we examine the problem of classification in large-scale and incrementally changing graphs. To this end, a framework combining an incremental Support Vector Machine (SVM) with the Weisfeiler-Lehman (W-L) graph kernel has been proposed to study this problem. By retaining the support vectors from each learning step, the classification model is incrementally updated whenever new changes are made to the subject graph. Furthermore, we design an entropy-based sub graph extraction strategy to select informative neighbor nodes and discard those with less discriminative power, to facilitate an effective classification process. We demonstrate the advantages of our learning techniques by conducting an empirical evaluation on two large-scale real-world graph datasets. The experimental results also validate the benefits of our sub graph extraction method when combined with the incremental learning techniques.