ABSTRACT
Network embedding aims to learn a low-dimensional vector representation for each node in the social and information networks, with the constraint to preserve network structures. Most existing methods focus on single network embedding, ignoring the relationship between multiple networks. In many real-world applications, however, multiple networks may contain complementary information, which can lead to further refined node embeddings. Thus, in this paper, we propose a novel multi-network embedding method, DMNE. DMNE is flexible. It allows different networks to have different sizes, to be (un)weighted and (un)directed. It leverages multiple networks via cross-network relationships between nodes in different networks, which may form many-to-many node mappings, and be associated with weights. To model the non-linearity of the network data, we develop DMNE to have a new deep learning architecture, which coordinates multiple neural networks (one for each input network data) with a co-regularized loss function. With multiple layers of non-linear mappings, DMNE progressively transforms each input network to a highly non-linear latent space, and in the meantime, adapts different spaces to each other through a co-regularized learning schema. Extensive experimental results on real-life datasets demonstrate the effectiveness of our method.
- Mikhail Belkin and Partha Niyogi. 2003. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. Vol. 15, 6 (2003), 1373--1396. Google ScholarDigital Library
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2015. Grarep: Learning graph representations with global structural information CIKM. ACM, 891--900. Google ScholarDigital Library
- Shaosheng Cao, Wei Lu, and Qiongkai Xu. 2016. Deep neural networks for learning graph representations AAAI. 1145--1152. Google ScholarDigital Library
- Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, and Thomas S Huang. 2015. Heterogeneous network embedding via deep architectures KDD. ACM, 119--128. Google ScholarDigital Library
- Wei Cheng, Xiang Zhang, Zhishan Guo, Yubao Wu, Patrick F Sullivan, and Wei Wang. 2013. Flexible and robust co-regularized multi-domain graph clustering KDD. ACM, 320--328. Google ScholarDigital Library
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks KDD. ACM, 135--144. Google ScholarDigital Library
- Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, and Chih-Jen Lin. 2008. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. Vol. 9, Aug (2008), 1871--1874. Google ScholarDigital Library
- Kwang-Il Goh, Michael E Cusick, David Valle, Barton Childs, Marc Vidal, and Albert-László Barabási. 2007. The human disease network. Proc. Natl. Acad. Sci. U.S.A. Vol. 104, 21 (2007), 8685--8690.Google ScholarCross Ref
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. ACM, 855--864. Google ScholarDigital Library
- Geoffrey E Hinton and Ruslan R Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science Vol. 313, 5786 (2006), 504--507.Google Scholar
- Xiao Huang, Jundong Li, and Xia Hu. 2017 a. Accelerated attributed network embedding. In SDM. SIAM, 633--641.Google Scholar
- Xiao Huang, Jundong Li, and Xia Hu. 2017 b. Label informed attributed network embedding. In WSDM. ACM, 731--739. Google ScholarDigital Library
- TaeHyun Hwang, Gowtham Atluri, MaoQiang Xie, Sanjoy Dey, Changjin Hong, Vipin Kumar, and Rui Kuang. 2012. Co-clustering phenome--genome for phenotype classification and disease gene discovery. Nucleic Acids Res. Vol. 40, 19 (2012), e146--e146.Google ScholarCross Ref
- Ming Ji, Jiawei Han, and Marina Danilevsky. 2011. Ranking-based classification of heterogeneous information networks KDD. ACM, 1298--1306. Google ScholarDigital Library
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks NIPS. 1097--1105. Google ScholarDigital Library
- Abhishek Kumar, Piyush Rai, and Hal Daume. 2011. Co-regularized multi-view spectral clustering. In NIPS. 1413--1421. Google ScholarDigital Library
- Daniel D Lee and H Sebastian Seung. 2001. Algorithms for non-negative matrix factorization. In NIPS. 556--562.Google Scholar
- Kar Wai Lim and Wray Buntine. 2015. Bibliographic analysis with the citation network topic model ACML. 142--158.Google Scholar
- Kar Wai Lim and Wray Buntine. 2016. Bibliographic analysis on research publications using authors, categorical labels and the citation network. Machine Learning Vol. 103, 2 (2016), 185--213. Google ScholarDigital Library
- Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. Vol. 9, Nov (2008), 2579--2605.Google Scholar
- Sofus A Macskassy and Foster Provost. 2007. Classification in networked data: A toolkit and a univariate case study. J. Mach. Learn. Res. Vol. 8, May (2007), 935--983. Google ScholarDigital Library
- Julian McAuley and Jure Leskovec. 2012. Learning to discover social circles in ego networks NIPS. 539--547. Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality NIPS. 3111--3119. Google ScholarDigital Library
- Jingchao Ni, Wei Cheng, Wei Fan, and Xiang Zhang. 2018. ComClus: A self-grouping framework for multi-network clustering. IEEE Trans. Knowl. Data Eng. Vol. 30, 3 (2018), 435--448.Google ScholarCross Ref
- Jingchao Ni, Hanghang Tong, Wei Fan, and Xiang Zhang. 2014. Inside the atoms: ranking on a network of networks KDD. ACM, 1356--1365. Google ScholarDigital Library
- Jingchao Ni, Hanghang Tong, Wei Fan, and Xiang Zhang. 2015. Flexible and robust multi-network clustering. In KDD. ACM, 835--844. Google ScholarDigital Library
- Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. 2016. Asymmetric transitivity preserving graph embedding KDD. ACM, 1105--1114. Google ScholarDigital Library
- Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. 2016. Tri-party deep network representation. In IJCAI. 1895--1901. Google ScholarDigital Library
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations KDD. ACM, 701--710. Google ScholarDigital Library
- Meng Qu, Jian Tang, Jingbo Shang, Xiang Ren, Ming Zhang, and Jiawei Han. 2017. An Attention-based Collaboration Framework for Multi-View Network Representation Learning. In CIKM. ACM, 1767--1776. Google ScholarDigital Library
- Jianbo Shi and Jitendra Malik. 2000. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. Vol. 22, 8 (2000), 888--905. Google ScholarDigital Library
- Daniel A Spielman and Shang-Hua Teng. 2004. Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems. In STOC. ACM, 81--90. Google ScholarDigital Library
- Jian Tang, Meng Qu, and Qiaozhu Mei. 2015 a. Pte: Predictive text embedding through large-scale heterogeneous text networks KDD. ACM, 1165--1174. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015 b. Line: Large-scale information network embedding. In WWW. International World Wide Web Conferences Steering Committee, 1067--1077. Google ScholarDigital Library
- Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks KDD. ACM, 990--998. Google ScholarDigital Library
- Joshua B Tenenbaum, Vin De Silva, and John C Langford. 2000. A global geometric framework for nonlinear dimensionality reduction. Science Vol. 290, 5500 (2000), 2319--2323.Google ScholarCross Ref
- Fei Tian, Bin Gao, Qing Cui, Enhong Chen, and Tie-Yan Liu. 2014. Learning deep representations for graph clustering AAAI. 1293--1299. Google ScholarDigital Library
- Hanghang Tong, Christos Faloutsos, and Jia-yu Pan. 2006. Fast random walk with restart and its applications ICDM. IEEE, 613--622. Google ScholarDigital Library
- Marc A Van Driel, Jorn Bruggeman, Gert Vriend, Han G Brunner, and Jack AM Leunissen. 2006. A text-mining analysis of the human phenome. Eur. J. Hum. Genet. Vol. 14, 5 (2006), 535.Google ScholarCross Ref
- Daixin Wang, Peng Cui, and Wenwu Zhu. 2016. Structural deep network embedding. In KDD. ACM, 1225--1234. Google ScholarDigital Library
- Weiran Wang, Raman Arora, Karen Livescu, and Jeff Bilmes. 2015. On deep multi-view representation learning. In ICML. 1083--1092. Google ScholarDigital Library
- Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. 2015. Network representation learning with rich text information IJCAI. 2111--2117. Google ScholarDigital Library
- Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. 2016. Homophily, structure, and content augmented network representation learning ICDM. IEEE, 609--618.Google Scholar
Index Terms
- Co-Regularized Deep Multi-Network Embedding
Recommendations
Cross-Network Embedding for Multi-Network Alignment
WWW '19: The World Wide Web ConferenceRecently, data mining through analyzing the complex structure and diverse relationships on multi-network has attracted much attention in both academia and industry. One crucial prerequisite for this kind of multi-network mining is to map the nodes ...
Structural Deep Network Embedding
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningNetwork embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the ...
Multi-view Heterogeneous Network Embedding
Knowledge Science, Engineering and ManagementAbstractIn the real world, the complex and diverse relations among different objects can be described in the form of networks. At the same time, with the emergence and development of network embedding, it has become an effective tool for processing ...
Comments