An on-line sequential learning method in social networks for node classification

Social networks have been a common platform for human interactions due to the rapid development of Internet. Along with the rising demand in network analysis, the issue of node classification have became an important research field. This article aims to address the task of node classification using an on-line sequential learning method with the role of links and node features. Compared to the conventional classification methods, we should not only use the node features for node classification, but also consider the interaction among the linked nodes. In this paper, we assume that the nodes have been partially labeled in a social network, and we use these labeled nodes to predict the categories of unlabeled nodes. Based on OS-ELM learning method, three node classification approaches are proposed. First, considering the influence of other nodes, we combine the linkage information with the features of the trained nodes to learn the classifier. Then, for reducing the learning time, we present a method to refine the node features. Finally, according to graph structures, we present an optimization node classification method. Extensive experiments were conducted to verify the performance of our proposed methods.