NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups
文献类型：期刊论文
| 作者 | Shen, Yinghan1,3; Jiang, Xuhui1,3; Li, Zijian1,3; Wang, Yuanzhuo1,2; Jin, Xiaolong3,5; Ma, Shengjie4; Cheng, Xueqi3,5 |
| 刊名 | KNOWLEDGE AND INFORMATION SYSTEMS
 |
| 出版日期 | 2022-08-23 |
| 页码 | 25 |
| 关键词 | Dynamic network completion Dynamic graph representation learning Social group Anonymous walk |
| ISSN号 | 0219-1377 |
| DOI | 10.1007/s10115-022-01724-2 |
| 英文摘要 | Real-world network data consisting of social interactions can be incomplete due to deliberately erased or unsuccessful data collection, which cause the misleading of social interaction analysis for many various time-aware applications. Naturally, the link prediction task has drawn much research interest to predict the missing edges in the incomplete social network. However, existing studies of link prediction cannot effectively capture the entangling topological and temporal dynamics already residing in the social network, thus cannot effectively reasoning the missing interactions in dynamic networks. In this paper, we propose the NEAWalk, a novel model to infer the missing social interaction based on topological-temporal features of patterns in the social group. NEAWalk samples the query-relevant walks containing both the historical and evolving information by focusing on the temporal constraint and designs a dual-view anonymization procedure for extracting both topological and temporal features from the collected walks to conduct the inference. Two-track experiments on several well-known network datasets demonstrate that the NEAWalk stably achieves superior performance against several state-of-the-art baseline methods. |
| 资助项目 | National Natural Science Foundation of China[U1836206] ; National Natural Science Foundation of China[91646120] ; National Natural Science Foundation of China[U21B2046] ; National Natural Science Foundation of China[62172393] ; National Key Research and Development Program of China[2018YTFB1402601] ; Zhongyuanyingcai program[204200510002] ; Major Public Welfare Project of Henan Province[201300311200] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000843458800002 |
| 出版者 | SPRINGER LONDON LTD |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/19456]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Wang, Yuanzhuo |
| 作者单位 | 1.Chinese Acad Sci, Data Intelligence Syst Res Ctr, Inst Comp Technol, Beijing, Peoples R China 2.Zhongke Big Data Acad, Zhengzhou, Peoples R China 3.Univ Chinese Acad Sci, Beijing, Peoples R China 4.Renmin Univ China, Gaoling Sch Artificial Intelligence, Beijing, Peoples R China 5.Chinese Acad Sci, Inst Comp Technol, Key Lab Network Data & Sci & Technol, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shen, Yinghan,Jiang, Xuhui,Li, Zijian,et al. NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups[J]. KNOWLEDGE AND INFORMATION SYSTEMS,2022:25. |
| APA | Shen, Yinghan.,Jiang, Xuhui.,Li, Zijian.,Wang, Yuanzhuo.,Jin, Xiaolong.,...&Cheng, Xueqi.(2022).NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups.KNOWLEDGE AND INFORMATION SYSTEMS,25. |
| MLA | Shen, Yinghan,et al."NEAWalk: Inferring missing social interactions via topological-temporal embeddings of social groups".KNOWLEDGE AND INFORMATION SYSTEMS (2022):25. |
入库方式： OAI收割
来源：计算技术研究所
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