A New Branch and Bound Algorithm for Mining Frequent Conceptual Links in Social Networks

Abstract

The frequent conceptual links is a descriptive data mining task which aims at describing a social network in term of the most connected type of nodes. This is done by grouping nodes into clusters or groups according to their attributes and checking the number of links between the nodes of each couple of groups, if this number is greater than a predefined threshold, the set of links is referred to as a frequent conceptual link (FCL). Although relatively recent, this task has received a number of research, chiefly in order to optimize the exploration of the search process. Indeed, the problem is defined as NP-hard, where the search process depends on the size of the network, the number of attributes and the set of their possible values whose combination can explode quickly. In this paper, we propose a new algorithm for mining the frequent conceptual links in a social network based on the technique of the branch and bound. In addition to defining an upper bound for the potential patterns in the search space, the algorithm implements other techniques which improve significantly the performance of the search process and allows to fix the shortcomings constated in the previous implementations.