Scalable Classification for Large Dynamic Networks

We examine the problem of node classification in large-scale and dynamically changing graphs. An entropy-based subgraph extraction method has been developed for extracting subgraphs surrounding the nodes to be classified. We introduce an online version of an existing graph kernel to incrementally compute the kernel matrix for a unbounded stream of these extracted subgraphs. After obtaining the kernel values, we adopt a kernel perceptron to learn a discriminative classifier and predict the class labels of the target nodes with their corresponding subgraphs. We demonstrate the advantages of our learning techniques by conducting empirical evaluations on two real-world graph datasets.