par Kumar, Rohit ;Calders, Toon
Référence CEUR Workshop Proceedings, 1929, page (3-6)
Publication Publié, 2017
Article révisé par les pairs
Résumé : Interaction networks differentiate themselves from the traditional networks in the sense that nodes interact continuously and repeatedly. Hence, when studying patterns in interaction networks it is essential to take into account the temporal nature of the data. In this paper, we present preliminary work on the problem of finding cyclic interaction patterns; for instance: one person transfers money to a second person, who transfers it to a third person transferring the money back to the first person. It is important here that the cycle occurs in the right temporal order, and that the time interval between the first and the last interaction of the cycle does not exceed a given time window. Cyclic patterns represent highly useful information; they are for instance used to detect specific types of fraud in financial transaction networks. Furthermore, as our results show, datasets from different domains show different behavior in terms of number and size of cycles. As such, cycles capture essential differences in temporal behavior of interaction networks.