Mining Regular Patterns in Weighted-Directed Networks

Abstract

Mining of regular patterns in dynamic networks finds immense application in characterizing the local properties of the networks, like behaviour (friendship relation), event occurrence (football matches). They in then are used to predict their future trends. But if they do not entail weight and direction aspects of the dynamic network, there can be loss of several significant details, such as strength of a relationship or event, specification of the person responsible for it in a relationship, winning or losing in case of events. To the best of our knowledge, no work has been reported yet to extract regular patterns that take into account weight and direction aspects of dynamic networks. We thus propose a novel method to mine regular patterns in weighted and directed networks. In the proposed method, different snapshots of the dynamic network are taken, and through the concept of Regular Expression, we obtain repetition rule for each of: occurrence sequence, weight sequence, direction sequence and weight-direction sequence. For each of these four categories, edges having same rule are grouped to obtain evolution patterns. To ensure the practical feasibility of the approach, experimental evaluation is done on the real world dataset of Enron emails. The results obtained show that, 2.39%, 6.92%, 9.96% and 1.81% of the edges are found to be regular on weight, direction, occurrence and weight-direction respectively.