A new irregular cellular learning automata-based evolutionary computation for time series link prediction in social networks

摘要

Link prediction (LP), as an attempt to predict event-based future connections within a network, is the main task of social network analysis (SNA). Accordingly, common LP approaches to forecast future connections utilize similarity metrics of non-connected links in a static network representation. A general shortcoming of most existing research studies in this field is that they tap the present condition of a system and fail to take any temporal events into account. Moreover; social networks are innately evolutionary since they are assumed to be online, non-deterministic, and unforeseeable in most applications. Consequently, it is not appropriate to employ deterministic models for examining actual social network problems. With regard to time-series LP (TSLP) problems, temporal evolution of connection incidence is correspondingly exploited to predict connection chances at a particular time. In this paper, a new TSLP method based on irregular cellular learning automaton (ICLA) and evolutionary computation (EC) is proposed. In the evolutionary procedure suggested here, each vertex (i.e. cell) includes a genome as well as a set of learning automata (LAs). Accordingly, the genome residing in a cell represents predicted links for the corresponding cell. Local information among cells in successive time 1 to T in the network is then analyzed to predict future connections in time T + 1. According to the distributed feature of the recommended approach, each genome is locally developed by a local search. The experiments in this study via e-mail and co-authorship networks ultimately show that the proposed algorithm leads to remarkable outcomes in predicting future connections.