1 |
SHRUM W, GENUTH J, CHOMPALOV I. Structures of Scientific Collaboration[M]. Cambridge: MIT Press, 2007:7-15. 10.7551/mitpress/7461.001.0001
|
2 |
EBADI A, SCHIFFAUEROVA A. How to become an important player in scientific collaboration networks?[J]. Journal of Informetrics, 2015, 9(4): 809-825. 10.1016/j.joi.2015.08.002
|
3 |
YANG C, SUN J S, MA J, et al. Scientific collaborator recommendation in heterogeneous bibliographic networks[C]// Proceedings of the 48th Hawaii International Conference on System Sciences. Piscataway: IEEE, 2015: 552-561. 10.1109/hicss.2015.73
|
4 |
KONG X J, JIANG H Z, WANG W, et al. Exploring dynamic research interest and academic influence for scientific collaborator recommendation[J]. Scientometrics, 2017, 113(1): 369-385. 10.1007/s11192-017-2485-9
|
5 |
PRADHAN T, PAL S. A multi-level fusion based decision support system for academic collaborator recommendation[J]. Knowledge-Based Systems, 2020, 197: No.105784. 10.1016/j.knosys.2020.105784
|
6 |
艾科,马国帅,杨凯凯,等. 一种基于集成学习的科研合作者潜力预测分类方法[J]. 计算机研究与发展, 2019, 56(7):1383-1395. 10.7544/issn1000-1239.2019.20180641
|
|
AI K, MA G S, YANG K K, et al. A classification method of scientific collaborator potential prediction based on ensemble learning[J]. Journal of Computer Research and Development, 2019, 56(7):1383-1395. 10.7544/issn1000-1239.2019.20180641
|
7 |
FORTUNATO S, BERGSTROM C T, BÖRNER K, et al. Science of science[J]. Science, 2018, 359(6379): No.eaao0185. 10.1126/science.aao0185
|
8 |
XIA F, WANG W, BEKELE T M, et al. Big scholarly data: a survey[J]. IEEE Transactions on Big Data, 2017, 3(1): 18-35. 10.1109/tbdata.2016.2641460
|
9 |
SHARMA D, KUMAR B, CHAND S. Recommending researchers in machine learning based on author-topic model[EB/OL]. (2021-09-05) [2022-05-31]..
|
10 |
TANG J, WU S, SUN J M, et al. Cross-domain collaboration recommendation[C]// Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2012: 1285-1293. 10.1145/2339530.2339730
|
11 |
周亦敏,黄俊. 基于BERT的学术合作者推荐研究[J]. 计算机技术与发展, 2021, 31(3):45-51. 10.3969/j.issn.1673-629X.2021.03.008
|
|
ZHOU Y M, HUANG J. Research on BERT-based academic collaborator recommendation[J]. Computer Technology and Development, 2021, 31(3): 45-51. 10.3969/j.issn.1673-629X.2021.03.008
|
12 |
蒲姗姗. 基于知识互补的科研合作专家推荐模型研究[J]. 情报理论与实践, 2018, 41(8): 96-101.
|
|
PU S S. Expert recommendation model in scientific and technical collaboration based on complementary knowledge[J]. Information Studies: Theory and Application, 2018, 41(8): 96-101.
|
13 |
黄璐,倪兴兴,程坷飞,等. 基于二模网络链路预测的合作者识别方法研究[J]. 情报学报, 2020, 39(9):906-913. 10.3772/j.issn.1000-0135.2020.09.003
|
|
HUANG L, NI X X, CHENG K F, et al. Identification of potential research partners based on two-mode network analysis[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(9):906-913. 10.3772/j.issn.1000-0135.2020.09.003
|
14 |
张鑫,文奕,许海云. 一种融合表示学习与主题表征的作者合作预测模型[J]. 数据分析与知识发现, 2021, 5(3): 88-100. 10.11925/infotech.2096-3467.2020.0515
|
|
ZHANG X, WEN Y, XU H Y. A prediction model with network representation learning and topic model for author collaboration[J]. Data Analysis and Knowledge Discovery, 2021, 5(3): 88-100. 10.11925/infotech.2096-3467.2020.0515
|
15 |
熊回香,杨雪萍,蒋武轩,等. 基于学术能力及合作关系网络的学者推荐研究[J]. 情报科学, 2019, 37(5): 71-78.
|
|
XIONG H X, YANG X P, JIANG W X, et al. Scholars recommend research based on academic competence and collaborative networks[J]. Information Science, 2019, 37(5): 71-78.
|
16 |
CHUAN P M, SON L H, ALI M, et al. Link prediction in co-authorship networks based on hybrid content similarity metric[J]. Applied Intelligence, 2018, 48(8): 2470-2486. 10.1007/s10489-017-1086-x
|
17 |
XIA F, CHEN Z, WANG W, et al. MVCWalker: random walk-based most valuable collaborators recommendation exploiting academic factors[J]. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3): 364-375. 10.1109/tetc.2014.2356505
|
18 |
林原,王凯巧,刘海峰,等. 网络表示学习在学者科研合作预测中的应用研究[J]. 情报学报, 2020, 39(4):367-373. 10.3772/j.issn.1000-0135.2020.04.003
|
|
LIN Y, WANG K Q, LIU H F, et al. Application of network representation learning in the prediction of scholar academic cooperation[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(4): 367-373. 10.3772/j.issn.1000-0135.2020.04.003
|
19 |
WANG W, LIU J Y, TANG T, et al. Attributed collaboration network embedding for academic relationship mining[J]. ACM Transactions on the Web, 2021, 15(1): No.4. 10.1145/3409736
|
20 |
艾科. 科研工作者合作潜力预测问题研究[D]. 太原:山西大学, 2019: 20-45. 10.7544/issn1000-1239.2019.20180641
|
|
AI K. Research on the prediction of researchers cooperation potential[D]. Taiyuan: Shanxi University, 2019: 20-45. 10.7544/issn1000-1239.2019.20180641
|
21 |
LEE J, LEE I, KANG J. Self-attention graph pooling[C]// Proceedings of the 36th International Conference on Machine Learning. New York: JMLR.org, 2019: 3734-3743.
|
22 |
FLORES-SZWAGRZAK K, TREIBICH R. Teamwork and individual productivity[J]. Management Science, 2020, 66(6): 2523-2544. 10.1287/mnsc.2019.3305
|
23 |
HAGEN N T. Harmonic allocation of authorship credit: Source-level correction of bibliometric bias assures accurate publication and citation analysis[J]. PLoS ONE, 2008, 3(12): No.e4021. 10.1371/journal.pone.0004021
|
24 |
STALLINGS J, VANCE E, YANG J S, et al. Determining scientific impact using a collaboration index[J]. Proceedings of the National Academy of Sciences of the United States of America, 2013, 110(24): 9680-9685. 10.1073/pnas.1220184110
|
25 |
KINGMA D P, BA J L. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30) [2022-05-22]..
|
26 |
JIN W, DERR T, WANG Y Q, et al. Node similarity preserving graph convolutional networks[C]// Proceedings of the 14th ACM International Conference on Web Search and Data Mining. New York: ACM, 2021: 148-156. 10.1145/3437963.3441735
|
27 |
BRODY S, ALON U, YAHAV E. How attentive are graph attention networks?[EB/OL]. (2022-01-31) [2022-05-31]..
|
28 |
LIU J L, ONG G P, CHEN X Q. GraphSAGE-based traffic speed forecasting for segment network with sparse data[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(3):1755-1766. 10.1109/tits.2020.3026025
|
29 |
JIN D, HUO C Y, LIANG C D, et al. Heterogeneous graph neural network via attribute completion[C]// Proceedings of the Web Conference 2021. Republic and Canton of Geneva: International World Wide Web Conferences Steering Committee: ACM, 2021: 391-400. 10.1145/3442381.3449914
|
30 |
LI Q B, WEN Z Y, HE B S. Practical federated gradient boosting decision trees[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2020: 4642-4649. 10.1609/aaai.v34i04.5895
|