Mining the temporal dimension of the information propagation

Abstract. In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Min-ing area has increased very fast. Among the possible related topics, the study of the information propagation in a network attracted the interest of many researchers, also from the industrial world. However, only a few answers to the questions “How does the information propagates over a network, why and how fast? ” have been discovered so far. On the other hand, these answers are of large interest, since they help in the tasks of finding experts in a network, assessing viral marketing strategies, identi-fying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a net-work with the help of two different techniques: TAS (Temporally Anno-tated Sequences) mining, aimed at extracting sequential patterns where each transition between two events is annotated with a typical transition time that emerges from input data, and Graph Mining, which is help-ful for locally analyzing the nodes of the networks with their properties. Finally we show preliminary results done in the direction of mining the information propagation over a network, performed on two well known email datasets, that show the power of the combination of these two approaches.