{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:47:37Z","timestamp":1753876057253,"version":"3.41.2"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T00:00:00Z","timestamp":1639958400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Division Of Mathematical Sciences, National Science Foundation","award":["1840203"],"award-info":[{"award-number":["1840203"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,12,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Network embeddings are a popular and effective pre-processing step when performing machine learning with network data. We demonstrate that standard boosting techniques, AdaBoost and Real AdaBoost can be applied to network embedding techniques to increase performance, particularly in terms of link prediction on test data in a cross-validation context. These approaches produce results competitive with other state-of-the-art embedding approaches when applied to a number of empirical networks. Additionally, we show on simulated data that Real AdaBoost can de-aggregate some networks, wherein networks created by two independent latent features can have those separate latent features inferred by different boosted rounds. Further analysis of the performance of these boosted methods shows that they retain the characteristic robustness to over-fitting as boosting methods in classical settings.<\/jats:p>","DOI":"10.1093\/comnet\/cnac001","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T12:18:06Z","timestamp":1643113086000},"source":"Crossref","is-referenced-by-count":0,"title":["Classically boosted network embeddings"],"prefix":"10.1093","volume":"10","author":[{"given":"Joel","family":"Nishimura","sequence":"first","affiliation":[{"name":"The School of Mathematical and Natural Sciences, Arizona State University, Glendale, AZ 85306-4908, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunpeng","family":"Zhao","sequence":"additional","affiliation":[{"name":"The School of Mathematical and Natural Sciences, Arizona State University, Glendale, AZ 85306-4908, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2022,2,15]]},"reference":[{"key":"2022021513082374400_B1","first-page":"1024","article-title":"Inductive representation learning on large graphs","author":"Hamilton,","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2022021513082374400_B2","doi-asserted-by":"crossref","first-page":"817","DOI":"10.1145\/1557019.1557109","article-title":"Relational learning via latent social dimensions","author":"Tang,","year":"2009","journal-title":"Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining"},{"key":"2022021513082374400_B3","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: problems, techniques, and applications","volume":"30","author":"Cai,","year":"2018","journal-title":"IEEE Trans. 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