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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (10): 28-36    DOI: 10.11925/infotech.2096-3467.2020.0062
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Predicting Research Collaboration Based on Translation Model
Chen Wenjie()
Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
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[Objective] This paper proposes a modified translation model (TransTopic) to predict research cooperation, aiming to promote exchanges among researchers and maximize efficiency.[Methods] We used TransTopic to uniformly map the nodes and edges of the scientific research cooperation network to low-dimensional vectors. First, we used the LDA model to extract the topic distribution features of stem cells papers. Then, we turned topic features to edge vectors with the deep autoencoder and obtained node vectors based on the translation mechanism. Finally, we predicted the scientific cooperation through the semantic calculation between the vectors.[Results] TransTopic’s AUC (95.21%) and MeanRank (17.48) indicators for link prediction are better than those of the existing models, and its topic prediction accuracy rate reached 86.52%.[Limitations] The proposed method only considered a one-step translation path, and did not fully utilized information like author’s institution, research interests, and publication levels.[Conclusions] The proposed method based on translation model could effectively predict research cooperation in the field of stem cells.

Key wordsTranslation Model      Deep Autoencoder      Topic Model      Link Prediction     
Received: 19 January 2020      Published: 09 November 2020
ZTFLH:  TP391  
Corresponding Authors: Chen Wenjie     E-mail:

Cite this article:

Chen Wenjie. Predicting Research Collaboration Based on Translation Model. Data Analysis and Knowledge Discovery, 2020, 4(10): 28-36.

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Translation Mechanism
Deep Autoencoder
Architecture of TransTopic
数据集 Net-S Net-M Net-L
节点集 59 586 354 740 632 893
边集 264 174 1 605 263 3 026 951
训练集 211 339 1 284 210 2 421 561
测试集 52 835 321 053 605 390
模型 Net-S Net-M Net-L
DeepWalk 59.26% 53.15% 52.45%
Node2Vec 72.42% 64.77% 62.95%
LINE 71.59% 65.62% 61.38%
TransE 84.83% 77.56% 69.42%
TransTopic 95.21% 87.13% 80.26%
模型 Net-S Net-M Net-L
DeepWalk 137.52 190.84 229.26
Node2Vec 83.34 134.48 171.04
LINE 92.81 105.64 159.44
TransE 39.40 60.89 87.32
TransTopic 17.48 26.45 51.27
排名 作者1 作者2 共同主题个数
1 Pearson, Bret J. Beerman, Isabel 20
2 Yu, Jennifer S. Rich, Jeremy N. 19
3 Chen, Chang-Zheng Hassanshahi, Mohammad 19
4 Zhu, Lian Wan, Wu 19
5 Miere, Cristian Wood, Victoria 18
Top 5 Partnerships
d Net-S Net-M Net-L
20 82.17% 80.95% 76.54%
70 86.52% 84.73% 81.65%
120 81.23% 79.24% 73.28%
Topic Prediction
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