Abstract
Patent analytics is a specialized branch of data analytics where patent documents are analysed to understand behavioural information. Citation network analysis is one of the common techniques to examine the importance of a patent by studying its citations. Typical patent citation network (PCN) will have millions of attributed nodes and edges. Inferencing on such a large network necessitates the use of attributed network embedding (ANE) techniques to bring down the computational requirements by reducing the dimensionality of the network data. Identifying the suitable ANE algorithm for PCN analytics is the purpose of this study. Multiple ANE algorithms are applied on the patent dataset to create low-dimensional embeddings, and these embeddings are used as the input for performing the innovation value prediction using linear regression model. Mean square error (MSE) is calculated between the predicted innovation values and the actual innovation values. MSE values obtained with different ANE algorithms are analysed to identify the most suitable ANE algorithm for patent analytics. GraphSAGE with mean-based aggregator resulted in the least MSE compared to all other ANE algorithms evaluated for patent analytics.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
D.M. Allen, Mean square error of prediction as a criterion for selecting variables. Technometrics 13(3), 469–475 (1971)
L. Aristodemou, F. Tietze, Exploring the Future of Patent Analytics (Cambridge, 2017)
E.M. Bergman, Embedding network analysis in spatial studies of innovation. Annals Regional Sci. 43(3), 559 (2009)
Z.D. Chengbin Hou, Openane: the first open source framework specialized in attributed network embedding. https://github.com/houchengbin/OpenANE (2018)
P.S. Crowther, R.J. Cox, A method for optimal division of data sets for use in neural networks, in International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (Springer, Berlin, 2005), pp. 1–7
P. Cui, X. Wang, J. Pei, W. Zhu, A survey on network embedding. IEEE Trans. Knowl. Data Eng. 31(5), 833–852 (2018)
H. Gao, H. Huang, Deep attributed network embedding. IJCAI 18, 3364–3370 (2018)
H. Gao, Z. Wang, S. Ji, Large-scale learnable graph convolutional networks, in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1416–1424 (2018)
S. GarcÃa, J. Luengo, F. Herrera, Data Preprocessing in Data Mining (Springer, Berlin, 2015)
A. Hagberg, P. Swart, D. Schult, Exploring network structure, dynamics, and function using networkx. Tech. rep., Los Alamos National Lab. (LANL), Los Alamos, NM (United States) (2008)
W.L. Hamilton, R. Ying, J. Leskovec, Inductive representation learning on large graphs, in NIPS (2017)
C. Hou, S. He, K. Tang, Attributed network embedding for incomplete attributed networks. arXiv preprint arXiv:1811.11728v2 (2019)
X. Hu, R. Rousseau, J. Chen, On the definition of forward and backward citation generations. J. Inform. 5(1), 27–36 (2011)
X. Huang, J. Li, X. Hu, Accelerated attributed network embedding, in Proceedings of the 2017 SIAM International Conference on Data Mining (SIAM, 2017), pp. 633–641
O. Kramer, Scikit-learn, in Machine Learning for Evolution Strategies (Springer, Berlin, 2016), pp. 45–53
M.H. Kutner, C.J. Nachtsheim, J. Neter, W. Li et al., Applied Linear Statistical Models, vol. 5 (McGraw-Hill, Irwin, NY, 2005)
J.O. Lanjouw, M. Schankerman, The quality of ideas: measuring innovation with multiple indicators (Tech. rep., National Bureau of Economic Research, 1999)
L. Liao, X. He, H. Zhang, T.S. Chua, Attributed social network embedding. arXiv preprint arXiv:1705.04969 (2017)
L. Liao, X. He, H. Zhang, T.S. Chua, Attributed social network embedding. IEEE Trans. Knowl. Data Eng. 30(12), 2257–2270 (2018)
H. Lin, H. Wang, D. Du, H. Wu, B. Chang, E. Chen, Patent quality valuation with deep learning models, in International Conference on Database Systems for Advanced Applications (Springer, Berlin, 2018), pp. 474–490
M. Palumbo, Commentary: cooperative patent classification: a new era for the world’s intellectual property offices. Technol. Innov. 15(2), 125–127 (2013)
PatentsView: Patents view. https://www.patentsview.org/. Accessed 17 Dec 2019
K. Potdar, T.S. Pardawala, C.D. Pai, A comparative study of categorical variable encoding techniques for neural network classifiers. Int. J. Comput. Appl. 175(4), 7–9 (2017)
G. Silverberg, B. Verspagen, The size distribution of innovations revisited: an application of extreme value statistics to citation and value measures of patent significance. J. Econ. 139(2), 318–339 (2007)
I. Von Wartburg, T. Teichert, K. Rost, Inventive progress measured by multi-stage patent citation analysis. Res. Policy 34(10), 1591–1607 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jose, J., Mary Saira Bhanu, S. (2021). Evaluation of Attributed Network Embedding Algorithms for Patent Analytics. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 735. Springer, Singapore. https://doi.org/10.1007/978-981-33-6977-1_23
Download citation
DOI: https://doi.org/10.1007/978-981-33-6977-1_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-33-6976-4
Online ISBN: 978-981-33-6977-1
eBook Packages: EngineeringEngineering (R0)