Machine Learning
A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Time-stamped graphs and their associated influence digraphs
Discrete Applied Mathematics
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
Mining sequences with temporal annotations
Proceedings of the 2006 ACM symposium on Applied computing
Pattern Mining in Frequent Dynamic Subgraphs
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for community identification in dynamic social networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Clinical Data with a Temporal Dimension: A Case Study
BIBM '07 Proceedings of the 2007 IEEE International Conference on Bioinformatics and Biomedicine
Time-Annotated Sequences for Medical Data Mining
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining temporally changing web usage graphs
WebKDD'04 Proceedings of the 6th international conference on Knowledge Discovery on the Web: advances in Web Mining and Web Usage Analysis
As time goes by: discovering eras in evolving social networks
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Evolving networks: Eras and turning points
Intelligent Data Analysis - Dynamic Networks and Knowledge Discovery
Hi-index | 0.00 |
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.