Name
Abstract | ||
|---|---|---|
User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11280-020-00833-8 | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Social network, Reinforcement learning, User identity linkage, Markov decision process | Journal | 24 |
Issue | ISSN | Citations |
1 | 1386-145X | 1 |
PageRank | References | Authors |
0.39 | 38 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| XiaoXue Li | 1 | 22 | 5.58 |
| Ya-nan Cao | 2 | 131 | 19.42 |
| Qian Li | 3 | 32 | 8.29 |
| Yanmin Shang | 4 | 1 | 0.72 |
| Li Yangxi | 5 | 34 | 5.75 |
| Yanbing Liu | 6 | 19 | 12.33 |
| Guandong Xu | 7 | 640 | 75.03 |