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One of the unique challenges in heterogeneous social networks is link prediction in new link types without verified link information, such as recommending products to new overseas groups. Existing link prediction models tend to learn type-specific knowledge on specific link types and predict missing or future links on the same link types. However, because of the uncertainty of new link types in the evolving process of social networks, it is difficult to collect sufficient verified link information in new link types. Therefore, we propose the Transferable Domain Adversarial Network (\n            <jats:italic>TDAN<\/jats:italic>\n            ) based on transfer learning to handle the challenge.\n            <jats:italic>TDAN<\/jats:italic>\n            exploits transferable type-shared knowledge in historical link types to help predict the unobserved links in new link types.\n            <jats:italic>TDAN<\/jats:italic>\n            mainly comprises a structural encoder, a domain discriminator, and an optimization decoder. The structural encoder learns the link representations in a heterogeneous social network. Subsequently, to learn transferable type-shared knowledge, the domain discriminator distinguishes link representations into different link types while minimizing the differences between type-specific knowledge in adversarial training. Inspired by the denoising auto-encoder, the optimization decoder reconstructs the learned type-shared knowledge to eliminate the noise generated during the adversarial training. Extensive experiments on Facebook and YouTube show that\n            <jats:italic>TDAN<\/jats:italic>\n            can outperform the state-of-the-art models.\n          <\/jats:p>","DOI":"10.1145\/3610229","type":"journal-article","created":{"date-parts":[[2023,7,18]],"date-time":"2023-07-18T12:26:18Z","timestamp":1689683178000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["TDAN: Transferable Domain Adversarial Network for Link Prediction in Heterogeneous Social Networks"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3162-2350","authenticated-orcid":false,"given":"Huan","family":"Wang","sequence":"first","affiliation":[{"name":"College of Informatics, Huazhong Agricultural University, PKU-Wuhan Institute for Artificial Intelligence, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6416-5398","authenticated-orcid":false,"given":"Guoquan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central China Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7968-2838","authenticated-orcid":false,"given":"Po","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central China Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,9,6]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2601435"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0378-8733(03)00009-1"},{"issue":"1","key":"e_1_3_2_4_2","first-page":"1","article-title":"Missing link prediction using common neighbor and centrality based parameterized algorithm","volume":"10","author":"Ahmad Iftikhar","year":"2020","unstructured":"Iftikhar Ahmad, Muhammad Usman Akhtar, Salma Noor, and Ambreen Shahnaz. 2020. 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