- Title
- Attributed collaboration network embedding for academic relationship mining
- Creator
- Wang, Wei; Liu, Jiaying; Tang, Tao; Tuarob, Suppawong; Xia, Feng; Gong, Zhiguo; King, Irwin
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/186099
- Identifier
- vital:16836
- Identifier
-
https://doi.org/10.1145/3409736
- Identifier
- ISBN:1559-1131 (ISSN)
- Abstract
- Finding 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. © 2020 ACM.
- Publisher
- Association for Computing Machinery
- Relation
- ACM Transactions on the Web Vol. 15, no. 1 (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2020 ACM
- Rights
- Open Access
- Subject
- 4605 Data management and data science; 4606 Distributed computing and systems software; 4608 Human-centred computing; Academic information retrieval; Graph learning; Network embedding; Scientific collaboration
- Full Text
- Reviewed
- Funder
- This work is partially supported by National Natural Science Foundation of China under Grant No. 61872054, the Fundamental Research Funds for the Central Universities (Grant No. DUT19LAB23), and the China Postdoctoral Science Foundation (Grant No. 2019M651115).
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