TY - GEN
T1 - Mining the temporal dimension of the information propagation
AU - Berlingerio, Michele
AU - Coscia, Michele
AU - Giannotti, Fosca
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=70349849868&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03915-7_21
DO - 10.1007/978-3-642-03915-7_21
M3 - Conference contribution
AN - SCOPUS:70349849868
SN - 3642039146
SN - 9783642039140
VL - 5772 LCNS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 237
EP - 248
BT - Advances in Intelligent Data Analysis VIII - 8th International Symposium on Intelligent Data Analysis, IDA 2009, Proceedings
T2 - 8th International Symposium on Intelligent Data Analysis, IDA 2009
Y2 - 31 August 2009 through 2 September 2009
ER -