Detecting changes in sequences of attributed graphs

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.