CSpace
Cross-Network Skip-Gram Embedding for Joint Network Alignment and Link Prediction
Du, Xingbo1; Yan, Junchi2; Zhang, Rui3; Zha, Hongyuan4
2022-03-01
Source PublicationIEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
ISSN1041-4347
Volume34Issue:3Pages:1080-1095
AbstractLink 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.
KeywordTask 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
DOI10.1109/TKDE.2020.2997861
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2018AAA0100704] ; NSFC[U1609220] ; NSFC[61672231] ; NSFC[61972250] ; NSFC[U19B2035]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Information Systems ; Engineering, Electrical & Electronic
WOS IDWOS:000752013800006
PublisherIEEE COMPUTER SOC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/59954
Collection中国科学院数学与系统科学研究院
Corresponding AuthorYan, Junchi
Affiliation1.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
Recommended Citation
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.
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