Abstract: Link prediction tremendously gained interest in the field of machine learning and data mining due to its real world applicability on various fields such as in social network analysis, biomedicine, e-commerce, scientific community, etc. Several link prediction methods have been developed which mainly focuses on the topological features of the network structure, to figure out the link prediction problem. Here, the main aim of this paper is to perform feature extraction from the given real time complex network using subgraph extraction technique and labeling of the vertices in the subgraph according to the distance from the vertex associated with each target link. This proposed model helps to learn the topological pattern from the extracted subgraph without using the topological properties of each vertex. The Geodesic distance measure is used in labeling of the vertices in the subgraph. The feature extraction is carried out with different size of the subgraph as K = 10and K = 15. Then the features are fit to different machine learning classification model. For the evaluation purpose, area under the ROC curve (AUC) metric is used. Further, comparative analysis of the existing link prediction methods is performed to have a clear picture of their variability in the performance of each network. Later, the experimental results obtained from different machine learning classifiers based on AUC metric have been presented. From the analysis, we can conclude that AdaBoost, Adaptive Logistic Regression, Bagging and Random forest maintain great performance comparatively on all the network. Finally, comparative analysis has been carried out between some best existing methods, and four best classification models, to make visible that link prediction based on classification models works well across several varieties of complex networks and solve the link prediction problem with superior performance and with robustness.
Keywords: Link prediction, geodesic distance, classification model, complex network, data mining
