{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:35Z","timestamp":1761176195208,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Accurate prediction of opinion evolution is crucial for understanding public opinion dynamics. The neural message passing mechanisms, which exhibit conceptual similarities with opinion dynamics, are capable of autonomously updating parameters through gradient-based optimization techniques, thereby establishing themselves as a surrogate model for investigating the evolution of opinions. However, existing methods face three key limitations: (1) Social weights between individuals rely solely on node attribute similarity, ignoring temporal dynamics; (2) Temporal features are modeled only through time derivatives, and lacking robustness to external noise; (3) There is a lack of research on real-world opinion data with network structures, especially open-source datasets. To tackle these challenges, we propose Temporal Adaptive Neural Message Passing, a deep learning framework that addresses opinion dynamics through adaptive message passing, noise-aware data fusion to enhance long-term forecasting. Furthermore, we release three large-scale real-world datasets to serve as benchmarks for future research. For evaluation, we conducted experiments on three real-world and three synthetic datasets, comparing our method with existing mechanistic models (including hybrid models) and non-mechanistic models. The results demonstrate that our method achieves the best performance across all datasets.<\/jats:p>","DOI":"10.3233\/faia251065","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:29Z","timestamp":1761126629000},"source":"Crossref","is-referenced-by-count":0,"title":["Temporal Adaptive Neural Message Passing for Opinion Dynamics"],"prefix":"10.3233","author":[{"given":"Ou","family":"Lizhen","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}]},{"given":"Yao","family":"Yiping","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}]},{"given":"Chen","family":"Kai","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}]},{"given":"Tang","family":"Wenjie","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:30Z","timestamp":1761126630000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251065","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}