{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:46Z","timestamp":1761176206729,"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>Incorporating dynamic characteristics into Graph Neural Networks (GNNs) enhances the understanding of dynamic graph evolution, optimizing temporal-spatial representations for real-world dynamic network problems. However, existing discrete-time dynamic graphs (DTDGs) models face two crucial problems that are difficulty in capturing long-term dependencies and the unbalance of performance and computational efficiency. To address these issues, we propose the AGS-DGNN framework for modeling dynamic graphs which includes two components, one is a dynamic GNN model based on an adaptive gradient smoothing mechanism and meta-learning strategy to generate node embeddings and the other is a Transformer model for these node embedding sequences. Specifically, AGS-DGNN first computes the frame-wise loss for the current snapshot and applies an Exponential Moving Average (EMA) mechanism to smooth the loss gradients, thereby generating local EMA gradients. Then, the local EMA gradients from previous snapshots are aggregated to form a global EMA gradient, enabling effective gradient propagation along the temporal dimension under a meta-learning strategy. Finally, the model adaptively integrates the local and global EMA gradients using a dynamic adjustment factor to update the GNN. Additionally, a Transformer with multi-head self-attention mechanism is applied on the embedding sequence of snapshots to enhance the robustness and performance for the framework. Experiments on six public datasets show the advantage of our AGS-DGNN compared with existing baselines, where it has reached the optimum in sixteen out of eighteen performance metrics.<\/jats:p>","DOI":"10.3233\/faia251099","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:23Z","timestamp":1761126683000},"source":"Crossref","is-referenced-by-count":0,"title":["AGS-DGNN: Dynamic Graph Neural Networks Based on Adaptive Gradient Smoothing"],"prefix":"10.3233","author":[{"given":"Fengxian","family":"Cheng","sequence":"first","affiliation":[{"name":"School of Computer Science, Qufu Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuefeng","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Qufu Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lihui","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Engineering, Qufu Normal University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:51:23Z","timestamp":1761126683000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251099","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]]}}}