Incremental Learning with Concept Drift Detection and Prototype-based Embeddings for Graph Stream Classification

arXiv:2404.02572v1 Announce Type: new
Abstract: 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 …

abstract arxiv challenges change classification concept cs.lg data data stream data streams detection distribution drift embeddings environments graph incremental knowledge mining modelling tool type