New Cellular Learning Automata as a framework for online link prediction problem

Abstract

ABSTRACT One of the main areas of research in Social Network Analysis (SNA) is Link Prediction (LP). The LP problem is useful in understanding the evolution mechanism of social networks, as well as in different applications such as recommendation systems, bioinformatics and marketing. In LP algorithms, prior network information is used to predict future connections in social networks. In this paper, we introduce a multi-wave cellular learning automaton (MWCLA) and use it to solve the LP problem in social networks. This model is a new CLA with a connected structure and a module of LAs in each cell where the neighbours of the cell module are its successors. The MWCLA method uses multiple waves at the same time in the network in order to improve convergence speed as well as accuracy. For predicting links in the social network, multiple waves can be used to consider different aspects of the network. Here we show that the model converges upon a stable and compatible configuration. Compared to some state-of-the-art approaches, MWCLA produces significantly better results when applied to the LP problem.