Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction
文献类型：期刊论文
| 作者 | Du, Xingbo1; Yan, Junchi2; Zhang, Rui3; Zha, Hongyuan4 |
| 刊名 | IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
 |
| 出版日期 | 2022-03-01 |
| 卷号 | 34期号:3页码:1080-1095 |
| 关键词 | Task analysis Predictive models Social network services Peer-to-peer computing Optimization Computational modeling Computer science Link prediction network alignment cross-network embedding skip-gram biased random walk |
| ISSN号 | 1041-4347 |
| DOI | 10.1109/TKDE.2020.2997861 |
| 英文摘要 | Link prediction and network alignment are two fundamental and interleaved tasks in network analysis. In this paper, we propose a novel cross-network embedding model under the Skip-gram framework, which alternately performs link prediction and network alignment by joint optimization. Vertex sequences, obtained via a biased random walk based on empirical mixture distributions, are used to train a Skip-gram based node embedding model. On one hand, based on the similarity in embedding space, network alignment can be effectively performed either with the initial ground truth alignments as seeds or from scratch. On the other hand, the proposed link prediction model involves training a supervised classifier by sampling a set of positive and negative edges. We also modify and incorporate the Collective Link Fusion (CLF) method under a Skip-gram framework and show that the new method can achieve better results in both tasks. Extensive experimental results show the state-of-the-art performance of our methods. |
| 资助项目 | National Key Research and Development Program of China[2018AAA0100704] ; NSFC[U1609220] ; NSFC[61672231] ; NSFC[61972250] ; NSFC[U19B2035] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000752013800006 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59953]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Yan, Junchi |
| 作者单位 | 1.East China Normal Univ, Sch Comp Sci & Software Engn, Shanghai 200062, Peoples R China 2.Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, MoE Key Lab Artificial Intelligence, AI Inst, Shanghai 200240, Peoples R China 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100864, Peoples R China 4.Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA |
| 推荐引用方式 GB/T 7714 | Du, Xingbo,Yan, Junchi,Zhang, Rui,et al. Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2022,34(3):1080-1095. |
| APA | Du, Xingbo,Yan, Junchi,Zhang, Rui,&Zha, Hongyuan.(2022).Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,34(3),1080-1095. |
| MLA | Du, Xingbo,et al."Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 34.3(2022):1080-1095. |
入库方式： OAI收割
来源：数学与系统科学研究院
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