Link Prediction for Authorship Association in Heterogeneous Network Using Streaming Classification

Abstract

Prediction of links or relations between the objects in any network is no longer a new task these days; in fact it has become a high rated area of research and has attracted many researchers seeking their contribution to the mentioned area. Research has seen an exponential growth over the passing years, and the active researchers do not hesitate in linking with fellow researchers working in same domain irrespective of their geographic location. However this in turn has generated a very complex network of objects and links which are needed to be analyzed and dealt with. Prediction of co-authorship is the sub domain of link prediction and with the increasing complexity of co-authorship network the authors are treated as heterogeneous entity not as homogeneous ones. The rule is simple analyze the data preprocess it, train the classifier according to desired classification rules and then get the classified form of data. But irrelevant features always reflect various impacts and issues on generation of a classifier and consequently the impact is sustained to further classification results. Therefore, this paper proposes streaming classification algorithm combined with Correlation based Feature selection as a solution to the stated problem. The consistent and relevant features are selected with the help of feature selection algorithm and then these features are classified with the help of streaming classification algorithm- Very Fast Decision Tree (VFDT). VFDT is a streaming classification algorithm and it takes the dataset in the form of continuous stream as an input. Finally the effectiveness of the proposed algorithm can be seen in the experimental results.