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- Kai Shu
- Arizona State University, Tempe, AZ
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- Suhang Wang
- Arizona State University, Tempe, AZ
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- Jiliang Tang
- Michigan State University, East Lansing, MI
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- Reza Zafarani
- Syracuse University, Syracuse, NY
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- Huan Liu
- Arizona State University, Tempe, AZ
書誌事項
- タイトル別名
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- A Review
この論文をさがす
説明
<jats:p>The increasing popularity and diversity of social media sites has encouraged more and more people to participate on multiple online social networks to enjoy their services. Each user may create a user identity, which can includes profile, content, or network information, to represent his or her unique public figure in every social network. Thus, a fundamental question arises -- can we link user identities across online social networks? User identity linkage across online social networks is an emerging task in social media and has attracted increasing attention in recent years. Advancements in user identity linkage could potentially impact various domains such as recommendation and link prediction. Due to the unique characteristics of social network data, this problem faces tremendous challenges. To tackle these challenges, recent approaches generally consist of (1) extracting features and (2) constructing predictive models from a variety of perspectives. In this paper, we review key achievements of user identity linkage across online social networks including stateof- the-art algorithms, evaluation metrics, and representative datasets. We also discuss related research areas, open problems, and future research directions for user identity linkage across online social networks.</jats:p>
収録刊行物
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- ACM SIGKDD Explorations Newsletter
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ACM SIGKDD Explorations Newsletter 18 (2), 5-17, 2017-03-22
Association for Computing Machinery (ACM)