Improving the Precision of Link Prediction in Multi-Relational Heterogeneous Social Networks Using Evolutionary Algorithm (EA)
Keywords:social networks, clustering, link prediction, evolutionary GA, AdaBoost algorithm
Fundamental changes have occurred in social interactions of the people with the advent and expansion of online social networks. With the expansion of social networks and the ever-increasing number of their users, the prediction of the users' relationships has turned into a difficult and complicated problem in these networks. Link prediction examines the links missing on the current network as well as the new links created in the future in social networks. Supervised and unsupervised methods can be used to predict the link. In unsupervised link-prediction method, the ranking of pair nodes is done only using one criterion, and in contrast to supervised link-prediction methods, they can complete the information from multiple scales and usually make real-world network model better. One of the methods proposed recently by Wang et al. states the problem of link prediction within the framework of supervised link prediction. This framework includes a re-weighing scheme based on the extracted features from high-profile interactions patterns across the network with great performance in link prediction, but in some supervised methods, it performs poorly not improving the precision of the link prediction. Thus, to solve the problem of link prediction, we introduce a new supervised link-prediction framework. Using the graph-edge clustering, supervised learning, and feature selection with EAs such as genetic algorithm (GA) in heterogeneous social networks, the link prediction problem was used. Furthermore, AdaBoost algorithm was applied to train learning models. In doing so, the DBLP scientific dataset was used. The results showed that feature selection using evolutionary GA improves the link prediction precision in social networks related to DBLP scientific publications by 5% in the logical regression model and 100% in the neural network and naive Bayes models. However, in the randomized forest model, precision is reduced by about 20%.
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