Yukuo Cen
ABSTRACT
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.
- Smriti Bhagat, Graham Cormode, and S Muthukrishnan. 2011. Node classification in social networks. In Social network data analytics. Springer, 115--148.Google Scholar
- Aleksandar Bojchevski and Stephan Günnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In ICLR'18.Google Scholar
- Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, and Thomas S Huang. 2015. Heterogeneous network embedding via deep architectures. In KDD'15. ACM, 119--128. Google ScholarDigital Library
- Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2018. A survey on network embedding. TKDE (2018).Google Scholar
- Jesse Davis and Mark Goadrich. 2006. The relationship between Precision-Recall and ROC curves. In ICML'06. ACM, 233--240. Google ScholarDigital Library
- Manlio De Domenico, Antonio Lima, Paul Mougel, and Mirco Musolesi. 2013. The anatomy of a scientific rumor. Scientific reports, Vol. 3 (2013), 2980.Google Scholar
- Yuxiao Dong, Nitesh V Chawla, and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In KDD'17. ACM, 135--144. Google ScholarDigital Library
- Santo Fortunato. 2010. Community detection in graphs. Physics reports, Vol. 486, 3--5 (2010), 75--174.Google Scholar
- Hongchang Gao and Heng Huang. 2018. Deep Attributed Network Embedding.. In IJCAI'18. 3364--3370. Google ScholarDigital Library
- Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD'16. ACM, 855--864. Google ScholarDigital Library
- Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS'17. 1024--1034. Google ScholarDigital Library
- James A Hanley and Barbara J McNeil. 1982. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, Vol. 143, 1 (1982), 29--36.Google ScholarCross Ref
- Ruining He and Julian McAuley. 2016. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In WWW'16. 507--517. Google ScholarDigital Library
- Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780. Google ScholarDigital Library
- Xiao Huang, Jundong Li, and Xia Hu. 2017a. Accelerated attributed network embedding. In SDM'17. SIAM, 633--641.Google Scholar
- Xiao Huang, Jundong Li, and Xia Hu. 2017b. Label informed attributed network embedding. In WSDM'17. ACM, 731--739. Google ScholarDigital Library
- Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).Google Scholar
- Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016).Google Scholar
- Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR'17.Google Scholar
- Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. 2018. Attributed social network embedding. TKDE, Vol. 30, 12 (2018), 2257--2270.Google ScholarDigital Library
- Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017. A structured self-attentive sentence embedding. ICLR'17.Google Scholar
- Weiyi Liu, Pin-Yu Chen, Sailung Yeung, Toyotaro Suzumura, and Lingli Chen. 2017. Principled multilayer network embedding. In ICDMW'17. IEEE, 134--141.Google ScholarCross Ref
- Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. 2015. Image-based recommendations on styles and substitutes. In SIGIR'15. ACM, 43--52. Google ScholarDigital Library
- Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. ICLR'13.Google Scholar
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS'13. 3111--3119. Google ScholarDigital Library
- Sankar K Pal and Sushmita Mitra. 1992. Multilayer Perceptron, Fuzzy Sets, Classifiaction. (1992).Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. Deepwalk: Online learning of social representations. In KDD'14. ACM, 701--710. Google ScholarDigital Library
- Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, and Jie Tang. 2019. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. In WWW'19. Google ScholarDigital Library
- Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. 2018. Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In WSDM'18. ACM, 459--467. 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'17. ACM, 1767--1776. Google ScholarDigital Library
- Chuan Shi, Binbin Hu, Xin Zhao, and Philip Yu. 2018b. Heterogeneous Information Network Embedding for Recommendation. TKDE (2018). Google ScholarDigital Library
- Yu Shi, Fangqiu Han, Xinran He, Carl Yang, Jie Luo, and Jiawei Han. 2018a. mvn2vec: Preservation and Collaboration in Multi-View Network Embedding. arXiv preprint arXiv:1801.06597 (2018).Google Scholar
- Yizhou Sun, Brandon Norick, Jiawei Han, Xifeng Yan, Philip S Yu, and Xiao Yu. 2013. Pathselclus: Integrating meta-path selection with user-guided object clustering in heterogeneous information networks. TKDD, Vol. 7, 3 (2013), 11. Google ScholarDigital Library
- Jian Tang, Meng Qu, and Qiaozhu Mei. 2015. Pte: Predictive text embedding through large-scale heterogeneous text networks. In KDD'15. ACM, 1165--1174. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. Line: Large-scale information network embedding. In WWW'15. 1067--1077. Google ScholarDigital Library
- Lei Tang and Huan Liu. 2009. Uncovering cross-dimension group structures in multi-dimensional networks. In SDM workshop on Analysis of Dynamic Networks. ACM, 568--575.Google Scholar
- Lei Tang, Suju Rajan, and Vijay K Narayanan. 2009. Large scale multi-label classification via metalabeler. In WWW'09. ACM, 211--220. Google ScholarDigital Library
- Lei Tang, Xufei Wang, and Huan Liu. 2009. Uncoverning groups via heterogeneous interaction analysis. In ICDM'09. IEEE, 503--512. Google ScholarDigital Library
- Ben Taskar, Ming-Fai Wong, Pieter Abbeel, and Daphne Koller. 2004. Link prediction in relational data. In NIPS'04. 659--666. Google ScholarDigital Library
- Jizhe Wang, Pipei Huang, Huan Zhao, Zhibo Zhang, Binqiang Zhao, and Dik Lun Lee. 2018. Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. KDD'18, 839--848. Google ScholarDigital Library
- Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. 2015. Network representation learning with rich text information.. In IJCAI'15. 2111--2117. Google ScholarDigital Library
- Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2016. Revisiting semi-supervised learning with graph embeddings. In ICML'16. 40--48. Google ScholarDigital Library
- Hongming Zhang, Liwei Qiu, Lingling Yi, and Yangqiu Song. 2018. Scalable Multiplex Network Embedding. In IJCAI'18. 3082--3088. Google ScholarDigital Library
- Zhen Zhang, Hongxia Yang, Jiajun Bu, Sheng Zhou, Pinggang Yu, Jianwei Zhang, Martin Ester, and Can Wang. 2018. ANRL: Attributed Network Representation Learning via Deep Neural Networks.. In IJCAI'18. 3155--3161. Google ScholarDigital Library
Index Terms
- Representation Learning for Attributed Multiplex Heterogeneous Network
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