{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:55:48Z","timestamp":1753890948692,"version":"3.41.2"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,11,17]],"date-time":"2023-11-17T00:00:00Z","timestamp":1700179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Big Data"],"abstract":"<jats:p>Numerous networks in the real world change with time, producing dynamic graphs such as human mobility networks and brain networks. Typically, the \u201cdynamics <jats:bold>on<\/jats:bold> graphs\u201d (e.g., changing node attribute values) are visible, and they may be connected to and suggestive of the \u201cdynamics <jats:bold>of<\/jats:bold> graphs\u201d (e.g., evolution of the graph topology). Due to two fundamental obstacles, modeling and mapping between them have not been thoroughly explored: (1) the difficulty of developing a highly adaptable model without solid hypotheses and (2) the ineffectiveness and slowness of processing data with varying granularity. To solve these issues, we offer a novel scalable deep echo-state graph dynamics encoder for networks with significant temporal duration and dimensions. A novel neural architecture search (NAS) technique is then proposed and tailored for the deep echo-state encoder to ensure strong learnability. Extensive experiments on synthetic and actual application data illustrate the proposed method's exceptional effectiveness and efficiency.<\/jats:p>","DOI":"10.3389\/fdata.2023.1274135","type":"journal-article","created":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T10:08:31Z","timestamp":1700302111000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Fast and adaptive dynamics-on-graphs to dynamics-of-graphs translation"],"prefix":"10.3389","volume":"6","author":[{"given":"Lei","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiqian","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chang-Tien","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"B1","first-page":"7505","article-title":"\u201cGenerative modeling for protein structures,\u201d","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","author":"Anand","year":"2018"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1451","DOI":"10.1007\/s00521-013-1364-4","article-title":"An experimental characterization of reservoir computing in ambient assisted living applications","volume":"24","author":"Bacciu","year":"2014","journal-title":"Neural Comp. 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