Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI) | Change detection, Concept drift, Anomaly detection, Dynamic/Evolving graph, Attributed graph, Stationarity, Graph matching, Embedding |
Field | DocType | Citations |
Graph theory,Anomaly detection,Embedding,Change detection,Vertex (geometry),Computer science,Markov chain,Theoretical computer science,Matching (graph theory),Concept drift | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniele Zambon | 1 | 8 | 2.16 |
| Lorenzo Livi | 2 | 304 | 25.67 |
| Cesare Alippi | 3 | 1040 | 115.84 |