Mining reccurrent patterns in a dynamic attributed graph

TitreMining reccurrent patterns in a dynamic attributed graph
Publication TypeConference Proceedings
Year of Publication2017
AuthorsCheng, Z, Flouvat, F, Selmaoui-Folcher, N
Conference NameThe Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
VolumeLNCS 10234
Pagination631-643
Conference LocationMay 23-26, 2017, Jeju, South Korea
Mots-clésconstraints, dynamic attributed graph, pattern mining
Abstract

A great number of applications require to analyze a single attributed graph that changes over time. This task is particularly complex because both graph structure and attributes associated with each node can change. In the present work, we focus on the discovery of recurrent patterns in such a graph. These patterns are sequences of subgraphs which represent recurring evolutions of subsets of nodes w.r.t. their attributes. Various constraints have been defined (frequency, volume, connectivity, non-redundancy and temporal continuity) and an original algorithm has been developed. Experiments performed on synthetic and real-world datasets

URLhttp://pakdd2017.snu.ac.kr/