GSCS – Graph Stream Classification with Side Information

Abstract

With the popularity of applications like Internet, sensor network and social network, which generate graph data in stream form, graph stream classification has become an important problem. Many applications are generating side information associated with graph stream, such as terms and keywords in authorship graph of research papers or IP addresses and time spent on browsing in web click graph of Internet users. Although side information associated with each graph object contains semantically relevant information to the graph structure and can contribute much to improve the accuracy of graph classification process, none of the existing graph stream classification techniques consider side information. In this paper, we have proposed an approach, Graph Stream Classification with Side information (GSCS), which incorporates side information along with graph structure by increasing the dimension of the feature space of the data for building a better graph stream classification model. Empirical analysis by experimentation on two real life data sets is provided to depict the advantage of incorporating side information in the graph stream classification process to outperform the state of the art approaches. It is also evident from the experimental results that GSCS is robust enough to be used in classifying graphs in form of stream.