Mining the Temporal Dimension of the Information Propagation

  • Michele Berlingerio
  • Michele Coscia
  • Fosca Giannotti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5772)


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 two well known email datasets, that show the power of the combination of these two approaches.


Social Network Analysis Frequent Pattern Closeness Centrality Annotate Sequence Graph Mining 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Michele Berlingerio
    • 1
  • Michele Coscia
    • 2
  • Fosca Giannotti
    • 3
  1. 1.IMT-LuccaLuccaItaly
  2. 2.Dipartimento di InformaticaPisaItaly
  3. 3.ISTI-CNRPisaItaly

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