UM
Attributed Collaboration Network Embedding for Academic Relationship Mining
Wang,Wei1; Liu,Jiaying3; Tang,Tao5; Tuarob,Suppawong2; Xia,Feng4; Gong,Zhiguo6; King,Irwin1
2021
Source PublicationACM Transactions on the Web
ISSN1559-1131
Volume15Issue:1
AbstractFinding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding.
Keywordacademic information retrieval graph learning Network embedding scientific collaboration
DOI10.1145/3409736
URLView the original
Language英语
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Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorTang,Tao
Affiliation1.Dalian University of Technology,China and University of Macau,Taipa,Macao
2.Dalian University of Technology,Dalian,China
3.Mahidol University,Nakhon Pathom,Thailand
4.Federation University Australia,Australia and Dalian University of Technology,Dalian,China
5.University of Macau,Macao
6.Chinese University of Hong Kong,Hong Kong
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Wang,Wei,Liu,Jiaying,Tang,Tao,et al. Attributed Collaboration Network Embedding for Academic Relationship Mining[J]. ACM Transactions on the Web,2021,15(1).
APA Wang,Wei,Liu,Jiaying,Tang,Tao,Tuarob,Suppawong,Xia,Feng,Gong,Zhiguo,&King,Irwin.(2021).Attributed Collaboration Network Embedding for Academic Relationship Mining.ACM Transactions on the Web,15(1).
MLA Wang,Wei,et al."Attributed Collaboration Network Embedding for Academic Relationship Mining".ACM Transactions on the Web 15.1(2021).
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