Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Patil, Megha Ashok; Kumar, Sunil; Kumar, Sandeep Title A name given to the resource GraphDrift-net: a dynamic graph-based framework for concept drift detection in short unstructured text streams Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived European Physical Journal Plus;Volume;140;Issue;9;Article No.;884; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1140/epjp/s13360-025-06812-0" target="_blank" rel="noreferrer noopener">https://doi.org/10.1140/epjp/s13360-025-06812-0</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105016363023?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105016363023?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Patil M.A., Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India; Kumar S., Amity School of Engineering and Technology, Amity University Rajasthan, Jaipur, India; Kumar S., CHRIST University, Karnataka, Bangalore, India Description An account of the resource Detecting concept drift in text streams is challenging due to the rapid evolution of language, shifting user behavior, and temporal dependencies. Issues like data sparsity, high dimensionality, lack of labeled data, and multimodal drift further complicate real-time detection and adaptation. This paper proposes GraphDrift-net, a novel dynamic graph-based framework for detecting and adapting to concept drift in evolving text streams. The model comprise of the following components: evolving Time BERT (EvoTimeBERT), which captures temporal language evolution via historical token memory and multi-scale temporal convolutions, hierarchical temporal graph network with dynamic topics and adaptive memory (HTGN-DTAM), a heterogeneous graph neural network that dynamically constructs topic-aware graphs to track changing semantics and Chronograph Detection, a time-series-based drift detection method leveraging graph statistics such as node centrality and clustering coefficient changes. In addition, graph neural reinforcement learning framework (GNRL), a reinforcement learning-based adaptive learning module, enables model adaptability by word embedding update, memory decay rate tuning, and few-shot adaptation. Experimental evaluations over various real-world datasets, including Twitter-1, Twitter-2, Enron, and News20, demonstrate that GraphDrift-net outperforms other methods in accuracy, F1-score, and drift detection sensitivity. The model achieves accuracy as high as 99.7%, is able to identify more drift points, and is more stable with computational efficiency, making it extremely appropriate for real-time text stream applications. The Author(s), under exclusive licence to SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2025. Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21905444; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online