Representing A Contentbased Link Prediction Algorithm in Scientific Social Networks

Predicting collaboration between two authors, using their research interests, is one of the important issues that couldimprove the group researches. one type of social networks is the coauthorship network that is one of the most widelyused data sets for studying. as a part of recent improvements of research, far much attention is devoted to thecomputational analysis of these social networks. the dynamics of these networks makes them challenging to study. linkprediction is one of the main problems in social networks analysis. if we represent a social network with a graph, linkprediction means predicting edges that will be created between nodes in the future. the output of link predictionalgorithms is using in the various areas such as recommender systems. also, collaboration prediction between two authorsusing their research interests is one of the issues that improve group researches. there are few studies on link predictionthat use content published by nodes for predicting collaboration between them. in this study, a new link predictionalgorithm is developed based on the people interests. by extracting fields that authors have worked on them via analyzingpapers published by them, this algorithm predicts their communication in future. the results of tests on sid dataset as coauthordataset show that developed algorithm outperforms all the structurebased link prediction algorithms. finally, thereasons of algorithm’s efficiency are analyzed and presented