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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (3): 88-100    DOI: 10.11925/infotech.2096-3467.2020.0515
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A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration
Zhang Xin1,Wen Yi1,2(),Xu Haiyun1,2
1Chengdu Library and Information Center, Chinese Academy of Sciences, Chengdu 610041, China
2Department of Library, Information and Archives Management, School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China
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[Objective] This paper proposes a method to predict scientific collaboration based on the network representation learning and author topic model. [Methods] First, we established the embedding vector representation of authors with the help of network representation learning method. Then, we calculated the structural similarity of authors with cosine similarity. Third, we obtained the topic representation of authors with the author-topic model, and computed the authors’ topic similarity with Hellinger distance. Finally, we linearly merged the two similarity measures, and used the Bayesian optimization method for the hyperparameter selection. [Results] We examined the proposed method with the NIPS datasets and found the best node2vec+ATM model after Bayesian parameter selection. It had an AUC value of 0.9271, which was 0.1856 higher than that of the benchmark model. [Limitations] We did not include the author’s institution and geographic location to the model. [Conclusions] The proposed model utilizes structure and content features to improve the prediction results of network representation learning.

Key wordsNetwork Representation Learning      Author Topic Model      Model Fusion      Scientific Collaboration Prediction     
Received: 03 June 2020      Published: 24 November 2020
ZTFLH:  G350  
Fund:National Natural Science Foundation of China(71704170);nformatization Project of the Chinese Academy of Sciences(XXH13506-203);Youth Innovation Promotion Association of the Chinese Academy of Sciences(2016159)
Corresponding Authors: Wen Yi     E-mail:

Cite this article:

Zhang Xin,Wen Yi,Xu Haiyun. A Prediction Model with Network Representation Learning and Topic Model for Author Collaboration. Data Analysis and Knowledge Discovery, 2021, 5(3): 88-100.

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Research Methods and Processes
DeepWalk Pipeline[15]
SDNE Model[19]
Probability Graph Representation of the Topic Model
基准算法 参数设置
LE 向量维数为128
Graph Factorization 向量维数为128
DeepWalk 向量维数为128,每个节点开始的随机游走数目为10,每个节点开始的随机游走步长为80,skip-gram模型窗口大小为10
LINE 向量维数为128,取混合一阶近似和二阶近似,负采样数目为5
node2vec 向量维数为128,游走参数p=0.25,q=0.25,每个节点开始的随机游走数目为10,每个节点开始的随机游走步长为80,skip-gram模型窗口大小为10
SDNE 向量维数为128,隐层神经元个数为1 000,控制一阶近似的超参数α=10-6,构造矩阵B的超参数β=5,自编码器中的L1损失参数μ1=10-5,L2损失参数μ2 =10-4,批次大小取200,学习率为0.01
TADW 向量维数为128
CANE 向量维数为128
Baseline Algorithms and Parameter Settings
Result of node2vec
主题 代表词
model human feature neuron spike task architecture response neural brain study input visual region population mechanism activity natural level implement
model inference process Bayesian structure variable gaussian approach latent distribution probabilistic variational tree datum method likelihood generative posterior Markov graphical
datum show result large method well scale propose number set achieve high performance dataset paper algorithm parameter require order experiment
network miss neural image learn deep object train representation layer convolutional recognition information recurrent code different learning noisy model visual
algorithm problem function method optimization result gradient convex show bound stochastic loss convergence study guarantee learning online set regret rate
learn feature datum method kernel task approach propose learning label base classification graph problem art clustering dataset cluster metric class
sample sparse distribution matrix estimate estimation analysis problem estimator point statistical dimensional non provide show high regression low error consider
time state dynamic learn system decision policy optimal search action approach control problem base information reinforcement space user reward value
Research Topics Extracted by Author-Topic Model
Authors’ Distribution Obtained by ATM
算法 正确率 召回率 AUC值
原模型 X+ATM 原模型 X+ATM 原模型 连接 X+ATM
ATM 0.003 0 1.000 0 0.500 0
LE 0.966 6 0.940 1 0.449 9 0.594 8 0.709 1 0.628 4 0.768 1
GF 0.996 0 0.993 0 0.008 3 0.046 9 0.505 7 0.608 2 0.521 4
DeepWalk 0.992 2 0.972 3 0.223 8 0.537 4 0.609 2 0.569 3 0.755 5
LINE 0.199 7 0.059 9 0.961 3 0.997 5 0.579 4 0.603 3 0.527 3
node2vec 0.979 5 0.878 8 0.502 1 0.811 4 0.741 5 0.735 0 0.845 2
SDNE 0.543 4 0.432 6 0.561 3 0.726 5 0.552 3 0.552 3 0.579 1
TADW 0.977 6 0.375 1 0.677 3
CANE 0.992 5 0.328 1 0.661 4
Results of the Fusion Model
迭代次数 α AUC值 迭代次数 α AUC值
1 0.417 0 0.862 8 8 0.560 2 0.922 1
2 0.720 3 0.881 7 9 0.592 7 0.922 9
3 0.000 1 0.500 0 10 0.581 3 0.927 1
4 0.302 3 0.777 6 11 0.580 5 0.926 8
5 1.000 0 0.809 7 12 0.580 2 0.926 6
6 0.577 4 0.926 0 13 0.578 8 0.926 3
7 0.599 2 0.924 2 14 0.578 4 0.926 2
Parameter Selection Process Based on Bayesian Optimization of node2vec+ATM Model
Bayesian Optimization Results
模型 最优融合参数 AUC值
LE+ATM 0.427 4 0.830 9
Graph Factorization+ATM 0.219 6 0.676 6
DeepWalk+ATM 0.344 9 0.835 4
LINE+ATM 0.999 9 0.577 5
node2vec+ATM 0.581 3 0.927 1
SDNE+ATM 0.359 2 0.555 5
Optimal Fusion Parameter Values for Several Fusion Models and Corresponding AUC Values
合作者 Yoshua Bengio Geoffrey E.Hinton Yann LeCun
1 Pascal Vincent Sam T. Roweis John S. Denker
2 David S. Touretzky Christopher K. I. Williams Rob Fergus
3 Samy Bengio Richard S. Zemel Corinna Cortes
4 Yann LeCun Max Welling Vladimir Vapnik
5 Ruslan R. Salakhutdinov Ilya Sutskever Yoshua Bengio
6 Mitsuo Kawato Ruslan R. Salakhutdinov Alex Waibel
7 John S. Denker Brendan J. Frey Bartlett W. Mel
8 Ilya Sutskever Peter Dayan Andrew Zisserman
9 Geoffrey E. Hinton Yee W. The Christof Koch
10 Yoram Singer Lawrence K. Saul Tomaso Poggio
Author Cooperation Prediction Results
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