  Data stream mining aims at extracting meaningful knowledge from continually
evolving data streams, addressing the challenges posed by nonstationary
environments, particularly, concept drift which refers to a change in the
underlying data distribution over time. Graph structures offer a powerful
modelling tool to represent complex systems, such as, critical infrastructure
systems and social networks. Learning from graph streams becomes a necessity to
understand the dynamics of graph structures and to facilitate informed
decision-making. This work introduces a novel method for graph stream
classification which operates under the general setting where a data generating
process produces graphs with varying nodes and edges over time. The method uses
incremental learning for continual model adaptation, selecting representative
graphs (prototypes) for each class, and creating graph embeddings.
Additionally, it incorporates a loss-based concept drift detection mechanism to
recalculate graph prototypes when drift is detected.
