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• W2912505106 abstract "Users are usually involved in multiple social networks, without explicit anchor links that reveal the correspondence among different accounts of the same user across networks. Anchor link prediction aims to identify the hidden anchor links, which is a fundamental problem for user profiling, information cascading, and cross-domain recommendation. Although existing methods perform well in the accuracy of anchor link prediction, the pairwise search manners on inferring anchor links suffer from big challenge when being deployed in practical systems. To combat the challenges, in this paper we propose a novel embedding and matching architecture to directly learn binary hash code for each node. Hash codes offer us an efficient index to filter out the candidate node pairs for anchor link prediction. Extensive experiments on synthetic and real world large-scale datasets demonstrate that our proposed method has high time efficiency without loss of competitive prediction accuracy in anchor link prediction." @default.
• W2912505106 created "2019-02-21" @default.
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• W2912505106 date "2019-05-13" @default.
• W2912505106 modified "2023-10-03" @default.
• W2912505106 title "Learning Binary Hash Codes for Fast Anchor Link Retrieval across Networks" @default.
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• W2912505106 doi "https://doi.org/10.1145/3308558.3313430" @default.
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