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 Book Chapter Faculty Publications- Book Chapter 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 Rappai, Sherin; Ramasamy, Gobi; Rohini, V.; Bagula, Antonie Title A name given to the resource Evaluating the Effectiveness of GraphSAGE with Reinforcement Learning in Suicide Risk Prediction Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Studies in Systems, Decision and Control;Volume;645;pp.465-476 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-10016-0_36" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-10016-0_36</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026820003?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026820003?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 Rappai S., Department of Computer Science, Kristu Jayanti College, Autonomous, Karnataka, Bengaluru, India; Ramasamy G., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Rohini V., Department of Computer Science, Christ (Deemed to be University), Karnataka, Bengaluru, India; Bagula A., Department of Computer Science, University of Western Cape, Cape Town, South Africa Description An account of the resource Suicide is considered to be a major mental health issue that has affected most individuals worldwide. According to World Health Organization, it shows the rise of suicidal rates among students has increased drastically. This vulnerability shows the rising need to encounter this issue with immediate effect. Therefore, proper detection methods have to be incorporated so that we can reduce the number of suicidal rates. Many computational models were implemented to address this issue. This study was conducted to compare various algorithms such as traditional machine learning models random forest and also various deep learning models like GraphSAGE, Graph Convolutional Network, Convolutional Neural Network, and Convolutional Neural Network with Long Short Term Memory with the proposed GraphSAGE Reinforcement Learning. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Adolescents; CNN; CNN-LSTM; Graph Convolutional Neural Network; GraphSAGE; Random Forest; Reinforcement learning; Suicide ideation Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 21984182; Language A language of the resource English Type The nature or genre of the resource Book chapter 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