Detecting changes in sequences of attributed graphs
Informazioni aggiuntive
Tipo
Contributo in atti di convegno
Anno
2017
Lingua
Inglese
Sommario
In several application domains, one deals with data described by elements and their pair-wise relations. Accordingly, graph theory offers a sound framework to cast modeling and analysis tasks. However, due to a multitude of reasons, real-world systems change operating conditions over time, hence calling for time-dependent, graph-based representations of systems' state. Here, we deal with this problem by considering a methodology for detecting changes in sequences of graphs. The adopted methodology allows to process attributed graphs with variable order and topology, and for which a one-to-one vertex correspondence at different time steps is not given. In practice, changes are recognized by embedding each graph into a vector space, where conventional change detection procedures exist and can be easily applied. Theoretical results are presented in a companion paper. In this paper, we introduce the methodology and focus on expanding experimental evaluations on controlled yet relevant examples involving geometric graphs and Markov chains.
Parole chiave
Anomaly detection, Attributed graph, Change detection, Concept drift, Dynamic/Evolving graph, Embedding, Graph matching, Stationarity
Titolo atti di convegno
2017 IEEE Symposium Series on Computational Intelligence (SSCI) Proceedings
Number ( Month )
November
Editore
IEEE
Nome convegno
IEEE Symposium Series on Computational Intelligence
Luogo convegno
Honolulu, Hawaii, USA
Data convegno
27 Nov.-1 Dec. 2017
ISBN
9781538627266
