Recommending diverse friends in signed social networks based on adaptive soft consensus paradigm using variable length genetic algorithm

Abstract

Despite the strategic role played by individuals, who act as intermediaries between distinct groups of people, the problem of recommending diverse friends in signed social networks (SSNs) still remains largely unexplored. Our model integrates homophily and diversity to develop an adaptive consensus based framework, which involves fuzzy group decision making analysis by leveraging on the signed social links and underlying users’ preferences, to offer lists of connections which are diverse as well as relevant. Our contributions are three-fold. First, we modeled the fuzzy binary adjacency relations between users, thereafter referred as decision makers (DMs), exploiting users’ preferences conferred on a set of items, and then higher order fuzzy m-ary adjacency relations are constructed to represent the grade of agreement between a set of m DMs. Further, in order to evaluate the relevance of each decision maker involved in the decision making process, we introduce a novel diversity measure based on the knowledge of socio-psychological theories and the information contained in social and interest links. Next, by employing variable-length genetic algorithm, an idea of adaptive consensus is explored to evolve groups of experts which are highly consensual as well as influential in the social network. Finally, on the basis of opinions gleaned from the members of these groups, sign of unknown links are predicted, thereby generating a top-N recommendations list of diverse friends. Extensive experimental study conducted on Epinions dataset illustrates that our proposed scheme outperforms the traditional graph-based methods.