Volume 29 Issue 6
Dec.  2020
Turn off MathJax
Article Contents
YE Zhonglin, ZHAO Haixing, ZHU Yu, XIAO Yuzhi. HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding[J]. Chinese Journal of Electronics, 2020, 29(6): 1141-1152. doi: 10.1049/cje.2020.10.001
Citation: YE Zhonglin, ZHAO Haixing, ZHU Yu, XIAO Yuzhi. HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding[J]. Chinese Journal of Electronics, 2020, 29(6): 1141-1152. doi: 10.1049/cje.2020.10.001

HSNR: A Network Representation Learning Algorithm Using Hierarchical Structure Embedding

doi: 10.1049/cje.2020.10.001
Funds:  This work is supported by the National Natural Science Foundation of China (No.11661069, No.61763041, No.61663041) and Key Research and Transformation Project in Qinghai Province (No.2020-GX-112).
More Information
  • Corresponding author: ZHAO Haixing (corresponding author) was born in 1969. He received his Doctor of Engineering Degree from the School of Computer Science in Northwestern Polytechnical University in 2004. He also received his Doctor of Science Degree from TWENTE University in Holland. He is a professor and part-time professor at Qinghai Normal University and Shaanxi Normal University, respectively. He is a director of Changjiang Scholars and Innovative Research Team in University. He is also the syndic of Operations Research Society, Combinatorics and Graph Theory Society in China. His research interests include complex network, semantic network and machine translation, hypergraph theory and database, network reliability etc. (Email:h.x.zhao@163.com)
  • Received Date: 2019-07-19
  • Publish Date: 2020-12-25
  • Vertices in the same group tend to connect densely, and usually share common attributes. Groups of different sizes reflect the relations of vertices in different ranges, and also reflect the features of different orders of the network. In this work, we propose a novel network representation learning algorithm by introducing group features of vertices of different orders to learn more discriminative network representations, named as network representation learning algorithm using Hierarchical structure embedding (HSNR). HSNR algorithm firstly constructs hierarchical relations of network structures of different orders based on greedy algorithm and modularity. In order to introduce hierarchical features into the network representation learning model, HSNR algorithm then introduces the idea of multi-relational modeling from knowledge representation, and converts the hierarchical relations into the triplet form between vertices. Finally, HSNR proposes a joint learning model embedding vertex triplets into the network representations. The experimental results show that the HSRN algorithm presented has an excellent performance in network vertex classification task on three real-world datasets.
  • loading
  • A. Theocharidis, S.V. Dongen, A.J. Enright, et al., "Network visualization and analysis of gene expression data using BioLayout Express3D", Nature Protocols, Vol.4, No.10, pp.1535-1550, 2009.
    S.P. Borgatti, A. Mehra, D.J. Brass, et al., "Network analysis in the social sciences", Science, Vol.323, No.5916, pp.892-895, 2009.
    Q. Wang, Z. Mao, B. Wang, et al., "Knowledge graph embedding:A survey of approaches and applications", IEEE Transactions on Knowledge & Data Engineering, Vol.29, No.12, pp.2724-2743, 2017.
    S. Bhagat, G. Cormode and S. Muthukrishnan, "Node classification in social networks", Computer Science, Vol. 16, No.3, pp.115-148, 2011.
    F. Wang, B. Zhang, S. Chai, et al., "Deep auto-encoded clustering algorithm for community detection in complex networks", Chinese Journal of Electronics, Vol.28, No.3, pp.489-496, 2019.
    P. Shen, S. Liu, L. Han, et al., "Distrust prediction in signed social network", Chinese Journal of Electronics, Vol.28, No.1, pp.189-194, 2019.
    W. Chen, L. Shi, W. Chen, et al., "A survey of macroscopic brain network visualization technology", Chinese Journal of Electronics, Vol.27, No.5, pp.889-899, 2018.
    Z. Tongzhen, S. Ruimin and L.U. Hongtao, "Using nonnegative matrix factorization to cluster learners and construct learning communities", Chinese Journal of Electronics, Vol.20, No.2, pp.207-211, 2011.
    M.E.J. Newman, "Modularity and community structure in networks", https://arxiv.org/pdf/physics/0602124.pdf, 2006-02-17.
    J.E. Gonzalez, R.S. Xin, A. Dave, et al., "GraphX:Graph processing in a distributed dataflow framework", Usenix Conference on Operating Systems Design & Implementation, Berkeley, CA, USA, pp.599-613, 2014.
    Y.C. Low, D. Bickson and J. Gonzalez, "Distributed GraphLab:A framework for machine learning and data mining in the cloud", https://arxiv.org/pdf/1204.6078.pdf, 2012-04-26.
    T. Mikolov, K. Chen, G. Corrado, et al., "Efficient estimation of word representations in vector space", https://arxiv.org/pdf/1301.3781.pdf, 2013-01-16.
    T. Mikolov, I. Sutskver, K. Chen, et al., "Distributed representations of words and phrases and their compositionality", Proceedings of Advances in Neural Information Processing Systems, South Lake Tahoe, California, USA, pp.3111-3119, 2013.
    B. Perozzi, R. Al-rfou and S. Skien, "DeepWalk:Online learning of social representations", Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp.701-710, 2014.
    H. Chen, B. Perozzi, R. Alrfou, et al., "A tutorial on network embeddings", https://arxiv.org/pdf/1808.02590.pdf, 2018-08-18.
    H.Y. Cai, V.W. Zheng and C.C.C. Chang, "A comprehensive survey of graph embedding:Problems, techniques, and applications", IEEE Transactions on Knowledge and Data Engineering, Vol.30, No.9, pp.1616-1637, 2018.
    S. Cao, W. Lu and Q. Xu, "GraRep:Learning graph representations with global structural information", Proceedings of ACM SIGKDD International on Conference on Information and Knowledge Management, Sydney, pp.891-900, 2015.
    Y. Bengio, A. Courville and P. Vincent, "Representation learning:A review and new perspectives", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.35, No.8, pp.1798-1828, 2013.
    C. Tu, X. Zeng, H. Wang, et al., "A unified framework for community detection and network representation learning", IEEE Transactions on Knowledge and Data Engineering, Vol.31, No.6, pp.1051-1065, 2019.
    A. Grover and J. Leskovec, "node2vec:Scalable feature learning for networks", Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, USA, pp.855-864, 2016.
    C. Yang, M.S. Sun, Z.Y. Liu, et al., "Fast network embedding enhancement via high order proximity approximation", Proceedings of International Joint Conference on Artificial Intelligence, Melbourne, Australia. pp.3894-3900, 2017.
    B. Perozzi, V. Kulkarni, H.C. Skiena, et al., "Don't walk, skip! online learning of multi-scale network embeddings", Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Sydney, Australia, pp.258-265, 2017.
    H.C. Chen, B. Perozzi, Y.F. Hu, et al., "HARP:Hierarchical representation learning for networks", the Thirty-Second AAAI Conference on Artificial Intelligence, Louisiana, USA, pp.2127-2134, 2018.
    J. Tang, M. Qu, M. Wang, et al., "LINE:Large-scale information network embedding", Proceedings of International World Wide Web Conferences Steering Committee, Florence, Italy, pp.1067-1077, 2015.
    T.N. Kipf and M. Welling, "Semi-supervised classification with graph convolutional networks", https://arxiv.org/pdf/1609.02907.pdf, 2016-09-09.
    J.Z. Li, J. Zhu and B. Zhang, "Discriminative deep random walk for network classification", Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, Berlin, German, pp.1004-1013, 2016.
    D. Wang, P. Cui and W. Zhu, "Structural deep network embedding", Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, pp.1225-1234, 2016.
    S.S. Cao, "Deep neural network for learning graph representations", Proceedings of Thirtieth AAAI Conference on Artificial Intelligence, Phoenxi, Arizona, USA, pp.1145-1152, 2016.
    J.X. Ma, P. Cui and W.W. Zhu, "Depth LGP:Learning embeddings of out-of-sample nodes in dynamic networks", the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pp.370-377, 2018.
    K. Tu, P. Cui, X. Wang, et al., "Structural deep embedding for hyper-networks", http://media.cs.tsinghua.edu.cn/multimedia/cuipeng/papers/DHNE.pdf, 2018-01-31.
    I.L. Goodfellow, J.K. Pouget-Abadie, M. Mirza, et al., "Generative adversarial networks", the Thirty-second Annual Conference on Neural Information Processing Systems, Palais des Congrès de Montréal, Montréal Canda, pp.187-208, 2018.
    Q.Y. Dai, Q. Li, J. Tang, et al., "Adversarial network embedding", the Thirty-Second AAAI Conference on Artificial Intelligence Conference, New Orleans, Louisiana, USA, pp.2167-2174, 2018.
    H.W. Wang, J. Wang, J.L. Wang, et al., "GraphGAN:Graph representation learning with generative adversarial nets", the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pp.2508-2515, 2018.
    A. Bojchevski, O. Shchur, D. Zügner, et al., "NetGAN:Generating graphs via random walks", Proceedings of Thirtyfifth International Conference on Machine Learning, 2018, Long Beach, California, USA, pp.609-618, 2018.
    C. Yang, Z.Y. Liu, D.L. Zhao, et al., "Network representation learning with rich text information", Proceedings of International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, pp.2111-2117, 2015.
    C.C. Tu, W. Zhang, Z.Y. Liu, et al., "Max-margin deepwalk:Discriminative learning of network representation", Proceedings of International Joint Conference on Artificial Intelligence, New York, US, pp.3889-3895, 2016.
    S. Pan, J. Wu, X. Zhu, et al., "Tri-party deep network representation", Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, New York, US, pp.1895-1901, 2016.
    X. Wang, P. Cui and J. Wang, "Community preserving network embedding", Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, pp.203-209, 2017.
    X. Huang, J. Li and X. Hu, "Accelerated attributed network embedding", Proceedings of SIAM International Conference on Data Mining, pp.633-641, 2017.
    Y. Zhang, T.S. Lyu and Y. Zhang, "COSINE:Communitypreserving social network embedding from Information diffusion cascades", Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pp.2620-2627, 2018.
    L.K. Zhou, Y. Yang, X. Ren, et al., "Dynamic network embedding by modeling triadic closure process", Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, Louisiana, USA, pp.571-578, 2018.
    S. Cavallari, V.W. Zheng, H.Y. Cai, et al., "Learning community embedding with community detection and node embedding on graphs", Proceedings of the 26th ACM International Conference on Information and Knowledge Management, Singapore, pp.377-386, 2017.
    N. Natarajan and I.S. Dhillon, "Inductive matrix completion for predicting gene-disease associations", Bioinformatics, Vol.30, No.12, pp.60-68, 2014.
    H. Huet, J. Biega and F.M. Suchanek, "Mining history with Le Monde", Proceedings of the 2013 Workshop on Automated Knowledge Base Construction, San Francisco, USA, pp.49-54, 2013.
    A. Bordes, N. Usunier and A. Garcia-Duran, "Translating embeddings for modeling multi-relational data", Proceedings of Twenty-seventh Conference on Neural Information Processing Systems, Harrah, USA, pp.2787-2795, 2013.
    R.E. Fan, K.W. Chang and C.J. Hsieh, "LIBLINEAR:A library for large linear classification", Journal of Machine Learning Research, Vol.9, No.9, pp.1871-1874, 2008.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (201) PDF downloads(60) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return