[Objective] This paper examines the structure of weighted network for patent transfers as well as the characteristics of agents, aiming to predict transaction opportunities and promote the connection of technology supply and demand. [Methods] First, we constructed a weighted network for patented technology transactions based on data from 2012 to 2016. Then, we used the entropy method to combine its structure and contents. Finally, we used the BP neural network to predict transaction opportunities and weights. [Results] The prediction accuracy by the proposed method, which combined the structure index RA and the content index Cosine, was the highest. The prediction error was also reduced by using the real and structure weights of the network to predict the link weight. [Limitations] More research is needed to study the Node properties and network evolution mechanism. [Conclusions] The link prediction method has a higher precision, which help us find potential supply and demand agents of the technology patent transfers.
武玉英, 孙平, 何喜军, 蒋国瑞. 新能源领域专利转让加权网络中主体间技术交易机会预测*[J]. 数据分析与知识发现, 2018, 2(11): 73-79.
Wu Yuying,Sun Ping,He Xijun,Jiang Guorui. Predicting Transactions Among Agents in Patent Transfer Weighted Networks for New Energy. Data Analysis and Knowledge Discovery, 2018, 2(11): 73-79.
(Lv Weimin, Wang Xiaomei, Han Tao.Recommending Scientific Research Collaborators with Link Prediction and Extremely Randomized Trees Algorithm[J]. Data Analysis and Knowledge Discovery, 2017, 1(4): 38-45.)
Lü L, Zhou T.Link Prediction in Complex Networks: A Survey[J]. Physical A: Statistical Machanics and Its Applications, 2011, 290(6): 1150-1170.
Symeonidis P, Tiakas E, Manolopoulos Y.Transitive Node Similarity for Link Prediction in Social Networks with Positive and Negative Links[C]// Proceedings of the 4th ACM Conference on Recommender Systems. ACM, 2010: 183-190.
Bai M, Hu K, Tang Y.Link Prediction Based on a Semi-Local Similarity Index[J]. Chinese Physics B, 2011, 20(12) : 498-504.
Zhu M, Cao T, Jiang X.Using Clustering Coefficient to Construct Weighted Networks for Supervised Link Prediction[J]. Social Network Analysis & Mining, 2014, 4(1): 1-8.
Lü L, Zhou T. Link Prediction in Weighted Networks: The Role of Weak Ties[J]. EPL, 2010, 89(1): Article No.18001.
Pan L, Zhou T, Lv L, et al. Predicting Missing Links and Identifying Spurious Links via Likelihood Analysis[J]. Scientific Reports, 2016, 6(5): Article No. 22955.
Sett N, Singh S R, Nandi S.Influence of Edge Weight on Node Proximity Based Link Prediction Methods: An Empirical Analysis[J]. Neurocomputing, 2016, 172(3): 71-83.
He Y L, Liu J N K, Hu Y X,et al. OWA Operator Based Link Prediction Ensemble for Social Network[J]. Expert Systems with Applications, 2015, 42(1): 21-50.
(Guo Jingfeng, Liu Miaomiao, Luo Xu.Link Prediction Based on Similarity of Nodes of Multipath in Weighted Social Networks[J]. Journal of Zhejiang University: Engineering Science, 2016, 50(7): 1347-1352.)
(Wang Jun, Yue Feng, Wang Gang, et al.Expert Recommendation in Scientific Social Network Based on Link Prediction[J]. Journal of Intelligence, 2015, 34(6): 151-157.)
(Hou Yinxiu, Li Weiqing, Wang Weijun, et al.Personalized Book Recommendation Based on User Preferences and Commodity Features[J]. Data Analysis and Knowledge Discovery, 2017, 1(8): 9-17.)
Aicher C, Jacobs A Z, Clauset A.Learning Latent Block Structure in Weighted Networks[J]. Journal of Complex Networks, 2014, 3(2): 1-28.
Zhao J, Miao L, Yang J, et al. Prediction of Links and Weights in Networks by Reliable Routes[J]. Scientific Reports, 2015, 5: Article No.12261.
Cui Y, Zhang L, Wang Q, et al.Heterogeneous Network Linkage-weight Based Link Prediction in Bipartite Graph for Personalized Recommendation[J]. Procedia Computer Science, 2016, 91: 953-958.
Wang L, Hu K, Tang Y.Robustness of Link-Prediction Algorithm Based on Similarity and Application to Biological Networks[J]. Current Bioinformatics, 2014, 9(5): 1-7.
(Zhou Puxiong, Zhang Bingrong, Zhao Longwen.Research on Contextual Information Recommendation Service Based on BP Neural Network[J]. Information Science, 2016, 34(3): 71-75.)
Sharma U, Minocha B.Link Prediction in Social Networks: A Similarity Score Based Neural Network Approach[C]// Proceedings of the 2nd International Conference on Information and Communication Technology for Competitive Strategies. ACM, 2016:90.
(Yan Jing, Bi Qiang, Li Jie, et al.Construction of Aggregation Quality Predicting Model for Digital Resource in Library——Based on Improved Genetic Algorithm and BP Neural Network[J]. Data Analysis and Knowledge Discovery, 2017, 1(12): 49-62.)
Lv L, Jin C H, Zhou T. Similarity Index Based on Local Paths for Link Prediction of Complex Networks[J]. Physical Review E, 2009, 80: Article No. 046122.
Das R, Zaheer M, Dyer C.Gaussian LDA for Topic Models with Word Embeddings[C]// Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics. 2015.
(Ren Haiying, Yu Liting, Huang Lucheng.A Method for Discovering Scientific Research Opportunities Based on Link Prediction[J]. Journal of Intelligence, 2016, 35(10): 53-58.)
Basri R, Jacobs D.Lambertian Reflectance and Linear Subspaces[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(2): 218-233.
Soundarajan S, Hopcroft J.Using Community Information to Improve the Precision of Link Prediction Methods[C]// Proceedings of the 21st International Conference on World Wide Web. ACM, 2012: 607-608.
(Chen Wei, Li Chuanyun, Zhou Wen, et al.Research on the Weighted Patent Cooperation Network Based on New Energy Vehicles[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(6): 563-572.)