Title: Graph-driven Autonomous Adaptation for Multi-stream Concept Drift
Primary area: learning on time series and dynamical systems
Keywords: Data streams; Concept drift; Graph neural network; Drift adaptation

Abstract: Multi-stream concept drift introduces substantial challenges beyond traditional single-stream scenarios, as inter-stream dependencies produce complex, evolving dynamics that place increasingly stringent demands on real-world forecasting tasks. Existing adaptation methods typically address drift in isolation, overlooking spatio-temporal correlations—not only between streams but also among drift events—and failing to enable synchronized adaptation across large-scale data streams. Furthermore, current graph-based approaches often rely on static, pre-defined embeddings, limiting adaptability in highly dynamic environments. To address these limitations, we propose GAMAD, a graph-driven autonomous adaptation framework for multi-stream concept drift that integrates spatio-temporal graph construction with dynamic and predictive adaptation mechanisms for multi-stream forecasting. GAMAD dynamically constructs correlation graphs from historical distributional statistics and subgraph structures, eliminating dependence on pre-defined topology and enabling generalizable representations. For online multi-stream evolution, it performs noise-tolerant windowing for accurate node-level drift detection, and then expands from drift-centric nodes to localized subgraphs based on current multi-stream correlations. To further enhance forecasting generalization, we employ a hierarchical topological matching strategy to retrieve and reuse previously observed drift patterns, enabling more predictable adaptation to inherently unpredictable drifts. Extensive experiments on three large-scale real-world datasets demonstrate that GAMAD consistently outperforms state-of-the-art baselines in forecasting performance. We also show applicability to recommendation scenarios, where continuous adaptation to evolving user preferences is essential. We release code at: https://anonymous.4open.science/r/GAMAD-6AAB.

TL;DR: A graph-driven autonomous adaptation framework for multi-stream concept drift that integrates spatio-temporal graph construction with dynamic and predictive adaptation mechanisms for multi-stream forecasting.