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These methods are not suitable for multiple social networks analysis in real-life. Deep learning methods based on graph embedding are restricted by the impact of the active privacy protection policy of users on the graph structure. In this paper, we propose a novel method which neutralizes the impact of users\u2019 evasion strategies. First, graph embedding with conditional estimation analysis is used to obtain a robust embedding vector space. Secondly, cross-network features space for supervised learning is constructed via the constraints of cross-network feature collisions. The combination of robustness enhancement and cross-network feature collisions constraints eliminate the impact of evasion strategies. Extensive experiments on large-scale real-life social networks demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of precision, adaptability, and robustness for the scenarios with evasion strategies.<\/jats:p>","DOI":"10.1145\/3548685","type":"journal-article","created":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T12:21:47Z","timestamp":1658146907000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["A Novel Cross-Network Embedding for Anchor Link Prediction with Social Adversarial Attacks"],"prefix":"10.1145","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5782-177X","authenticated-orcid":false,"given":"Huanran","family":"Wang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5985-7648","authenticated-orcid":false,"given":"Wu","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9768-3121","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1177-3693","authenticated-orcid":false,"given":"Dapeng","family":"Man","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5502-7217","authenticated-orcid":false,"given":"Jiguang","family":"Lv","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China"}]}],"member":"320","published-online":{"date-parts":[[2022,11,7]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10179"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46128-1_29"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403201"},{"key":"e_1_3_1_6_2","article-title":"Can adversarial network attack be defended?","volume":"1903","author":"Chen Jinyin","year":"2019","unstructured":"Jinyin Chen, Yangyang Wu, Xiang Lin, and Qi Xuan. 2019. 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