Mining the information propagation in a network

Michele Berlingerio, Michele Coscia, Fosca Giannotti

Research output: Contribution in Book/Catalog/Report/Conference proceedingConference contribution

Abstract

In the last decade, Social Network Analysis has been a field in which the effort devoted from several researchers in the Data Mining 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, identifying fast or slow paths of the information inside a collaborative network. In this paper we study the problem of finding frequent patterns in a network with the help of two different techniques: TAS (Temporally Annotated 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 helpful 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 the well known Enron dataset, that show the power of the combination of these two approaches.

Original languageEnglish
Title of host publication17th Italian Symposium on Advanced Database Systems, SEBD 2009
Pages333-340
Number of pages8
Publication statusPublished - 2009
Externally publishedYes
Event17th Italian Symposium on Advanced Database Systems, SEBD 2009 - Camogli, Genova, Italy
Duration: 21 Jun 200924 Jun 2009

Conference

Conference17th Italian Symposium on Advanced Database Systems, SEBD 2009
Country/TerritoryItaly
CityCamogli, Genova
Period21/06/0924/06/09

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