Abstract
In this paper, traditional DeepWalk method and broad learning system (BLS) are used to classify network nodes in graph embedding, and results of classification are compared. When categorizing, DeepWalk adopts one vs rest (OvR) logistic regression method, and BLS is applied after the production of vector representations. In order to obviously compare results of the two classification methods, Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are employed to carry out the experiment on multi-label classification of BlogCatalog. The experimental result shows that F1 score of BLS is obviously higher than DeepWalk and other methods, and training time of BLS is much less than other methods. These performances make our method suitable to graph embedding.
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
Similar content being viewed by others
References
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 701–710 (2014)
Chen, C.L.P., Liu, Z.L.: Broad learning system: an effective and efficient incremental learning system without the need for deep architecture. IEEE Trans. Neural Netw. Learn. Syst. 29(1), 10–24 (2017)
Fouss, F., Pirotte, A., Renders, J.-M., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Andersen, R., Chung, F., Lang, K.: Local graph partitioning using pagerank vectors. In: 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2006, pp. 475–486. IEEE (2006)
Spielman, D.A., Teng, S.-H.: Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In: Proceedings of the Thirty-Sixth Annual ACM Symposium on Theory of Computing, pp. 81–90. ACM (2004)
Chen, C.L.P., Liu, Z., Feng, S.: Universal approximation capability of broad learning system and its structural variations. IEEE Trans. Neural Netw. Learn. Syst. 30, 1–14 (2018)
Tang, L., Liu, H.: Relational learning via latent social dimensions. In: Proceedings of the 15th ACM SIGKDD, KDD 2009, New York, USA, pp. 817–826 (2009)
Acknowledgments
This work is supported in part by the National Natural Science Foundation of China (under Grant Nos. 6175202, 61751205, 61572540, U1813203, 61803064, 71831002); the LiaoNing Revitalization Talents Program (under Grant No. XLYC1807046); the Science & Technology Innovation Funds of Dalian (under Grant no. 2018J11CY022); the Program for Innovative Research Team in University (IRT_17R13) and the Fundamental Research Funds for the Central Universities (3132019501, 3132019502).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Jiang, L., Zuo, Y., Li, T., Chen, C.L.P. (2019). A Fast Approach of Graph Embedding Using Broad Learning System. In: Lin, JW., Ting, IH., Tang, T., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2019. Communications in Computer and Information Science, vol 1131. Springer, Singapore. https://doi.org/10.1007/978-981-15-1758-7_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-1758-7_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1757-0
Online ISBN: 978-981-15-1758-7
eBook Packages: Computer ScienceComputer Science (R0)