MEMN:Multiple Vectors Embedding for Multi-Label Networks
文献类型：期刊论文
| 作者 | Liu, Zhuang3,4; Pu, Juhua3,4; Liu, Xingwu1,2; Chen, Yujun3,4 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2018 |
| 卷号 | 6页码:66143-66152 |
| 关键词 | Network embedding multiple vectors multi-label classification link prediction |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2018.2878870 |
| 英文摘要 | Network embedding, which assigns vectors to network nodes in a manner that preserves the network features, is a hotspot of network research in recent years. A salient common feature of the existing approaches is that each node is mapped to exactly one vector. This one-vector mapping is insufficient to represent the nodes' attribution in those extensively existed networks whose nodes' have multiple labels. In this paper, we present MEMN, a novel approach of multiple vectors embedding for multi-labeled networks. For any node in the network, MEMN employs Node2vecWalk to generate its neighbor nodes. We maintain a neighbor cluster center for each label of the node and induce its label by clustering the embeddings of the neighbor nodes. Then, we assign vectors, one per label, to the node. This method can be non-parameterized, namely, NP-MEMN method. That is, if the number of label vectors for a node is not given, NP-MEMN can learn during embedding. Empirical studies on real datasets show that either MEMN or NP-MEMN outperforms many widely used methods in both multi-label classification and link prediction. |
| 资助项目 | National Key Research and Development Program of China[2017YFB1002000] ; National Natural Science Foundation of China[61502320] ; Science Foundation of Shenzhen City in China ; State Key Laboratory of Software Development Environment |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000452407300001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/3504]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Liu, Xingwu |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 3.Beihang Univ, Engn Res Ctr ACAT, Minist Educ, Beijing 100191, Peoples R China 4.Beihang Univ, Res Inst, Shenzhen 518057, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Zhuang,Pu, Juhua,Liu, Xingwu,et al. MEMN:Multiple Vectors Embedding for Multi-Label Networks[J]. IEEE ACCESS,2018,6:66143-66152. |
| APA | Liu, Zhuang,Pu, Juhua,Liu, Xingwu,&Chen, Yujun.(2018).MEMN:Multiple Vectors Embedding for Multi-Label Networks.IEEE ACCESS,6,66143-66152. |
| MLA | Liu, Zhuang,et al."MEMN:Multiple Vectors Embedding for Multi-Label Networks".IEEE ACCESS 6(2018):66143-66152. |
入库方式： OAI收割
来源：计算技术研究所
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