{"type":"ScholarlyArticle","title":"Deep Attributed Network Embedding","authors":[{"name":"Hongchang Gao","slug":"hongchang-gao","given":"Hongchang","family":"Gao","affiliation":"Department of Electrical and Computer Engineering University of Pittsburgh , USA"},{"name":"Heng Huang","slug":"heng-huang","given":"Heng","family":"Huang","affiliation":"Department of Electrical and Computer Engineering University of Pittsburgh , USA"}],"abstract":"Network embedding has attracted a surge of attention in recent years. It is to learn the low-dimensional representation for nodes in a network, which benefits downstream tasks such as node classification and link prediction. Most of the existing approaches learn node representations only based on the topological structure, yet nodes are often associated with rich attributes in many real-world applications. Thus, it is important and necessary to learn node representations based on both the topological structure and node attributes. In this paper, we propose a novel deep attributed network embedding approach, which can capture the high non-linearity and preserve various proximities in both topological structure and node attributes. At the same time, a novel strategy is proposed to guarantee the learned node representation can encode the consistent and complementary  information from the topological structure and node attributes. Extensive experiments on benchmark datasets have verified the effectiveness of our proposed approach.","doi":"10.24963/ijcai.2018/467","url":"https://ijcai.org/proceedings/2018/467","pdf":"https://ijcai.org/proceedings/2018/0467.pdf","markdown":"https://ijcai.org/proceedings/2018/467.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/467","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":467,"pages":{"first":3364,"last":3370},"keywords":["Machine Learning Applications: Networks","Machine Learning Applications: Applications of Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Belkin and Niyogi, 2001] Mikhail Belkin and Partha Niyogi. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, volume 14, pages 585-591, 2001.","title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","authors":"Niyogi Belkin, Mikhail Belkin, Partha Niyogi","year":"2001","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai Xu. Grarep: Learning graph representations with global structural information. In Proceedings of the 24th ACM International on Conference on Information and Knowl- edge Management, pages 891-900. ACM, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Gao and Huang, 2018] Hongchang Gao and Heng Huang. Self-paced network embedding. In KDD, 2018.","title":"Self-paced network embedding","authors":"Huang ; ] Hongchang Gao, Heng Gao, Huang","year":"2018","venue":"KDD","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Gao et al., 2015a] Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan, Joaquin Goni Cortes, Yang Wang, Feiping Nie, John West, Andrew Saykin, Li Shen, et al. Identify- ing connectome module patterns via new balanced multi- graph normalized cut. In International Conference on Medical Image Computing and Computer-Assisted Inter- vention, pages 169-176. Springer, 2015.","title":"Identifying connectome module patterns via new balanced multigraph normalized cut","authors":"Gao","year":"2015","venue":"International Conference on Medical Image Computing and Computer-Assisted Intervention","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Gao et al., 2015b] Hongchang Gao, Feiping Nie, Xuelong Li, and Heng Huang. Multi-view subspace clustering. In Proceedings of the 2015 IEEE International Confer- ence on Computer Vision (ICCV), pages 4238-4246. IEEE Computer Society, 2015.","title":"Multi-view subspace clustering","authors":"Gao","year":"2015","venue":"Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hamilton et al., 2017] Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems, pages 1025-1035, 2017.","title":"Inductive representation learning on large graphs","authors":"Hamilton","year":"2017","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He and Niyogi, 2003] Xiaofei He and Partha Niyogi. Lo- cality preserving projections. In NIPS, volume 16, 2003.","title":"Locality preserving projections","authors":"Xiaofei He, Partha Niyogi","year":"2003","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2017a] Xiao Huang, Jundong Li, and Xia Hu. Accelerated attributed network embedding. In Proceed- ings of the 2017 SIAM International Conference on Data Mining, pages 633-641. SIAM, 2017.","title":"Accelerated attributed network embedding","authors":"Huang","year":"2017","venue":"Proceedings of the 2017 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2017b] Xiao Huang, Jundong Li, and Xia Hu. Label informed attributed network embedding. In Pro- ceedings of the Tenth ACM International Conference on Web Search and Data Mining, pages 731-739. ACM, 2017.","title":"Label informed attributed network embedding","authors":"Huang","year":"2017","venue":"Proceedings of the Tenth ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jiang et al., 2016] Wenhao Jiang, Hongchang Gao, Fu-lai Chung, and Heng Huang. The l2, 1-norm stacked robust autoencoders for domain adaptation. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pages 1723-1729. AAAI Press, 2016.","title":"The l2, 1-norm stacked robust autoencoders for domain adaptation","authors":"Wenhao Jiang, Hongchang Gao, Fu-Lai Chung, Heng Huang","year":"2016","venue":"Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kipf and Welling, 2016a] Thomas N Kipf and Max Welling. Semi-supervised classification with graph con- volutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"Welling ; Kipf, N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Kipf and Welling, 2016b] Thomas N Kipf and Max Welling. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308, 2016.","title":"Variational graph auto-encoders","authors":"Welling ; Kipf, N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"Maas et al., ] Andrew L Maas, Awni Y Hannun, and An- drew Y Ng. Rectifier nonlinearities improve neural net- work acoustic models. [Maaten and Hinton, 2008] Laurens van der Maaten and Ge- offrey Hinton. Visualizing data using t-sne. Journal of Machine Learning Research, 9(Nov):2579-2605, 2008.","title":"Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne","authors":"Maas","year":"2008","venue":"Maaten and Hinton","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Marsden and Friedkin, 1993] Peter V Marsden and Noah E Friedkin. Network studies of social influence. Sociological Methods & Research, 22(1):127-151, 1993.","title":"Network studies of social influence","authors":"Friedkin ; Marsden, V Peter, Noah E Marsden, Friedkin","year":"1993","venue":"Sociological Methods & Research","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[McPherson et al., 2001] Miller McPherson, Lynn Smith- Lovin, and James M Cook. Birds of a feather: Homophily in social networks. Annual review of sociology, 27(1):415- 444, 2001.","title":"Birds of a feather: Homophily in social networks","authors":"Mcpherson","year":"2001","venue":"Annual review of sociology","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pan et al., 2016] S Pan, J Wu, X Zhu, C Zhang, and Y Wang. Tri-party deep network representation. In Inter- national Joint Conference on Artificial Intelligence. AAAI Press/International Joint Conferences on Artificial Intelli- gence, 2016.","title":"Tri-party deep network representation","authors":"Pan, X Wu, C Zhu, Y Zhang, Wang","year":"2016","venue":"International Joint Conference on Artificial Intelligence. AAAI Press/International Joint Conferences on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of the 20th ACM SIGKDD in- ternational conference on Knowledge discovery and data mining, pages 701-710. ACM, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Salakhutdinov and Hinton, 2007] Ruslan Salakhutdi- nov and Geoffrey Hinton. Semantic hashing. RBM, 500(3):500, 2007.","title":"Ruslan Salakhutdinov and Geoffrey Hinton. Semantic hashing","authors":"Hinton Salakhutdinov","year":"2007","venue":"RBM","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of the 24th International Conference on World Wide Web, pages 1067-1077. International World Wide Web Confer- ences Steering Committee, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of the 24th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In Proceed- ings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1225- 1234. ACM, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Yang et al., 2010] Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juanzi Li, Li Zhang, and Zhong Su. Understanding retweeting behaviors in social networks. In Proceedings of the 19th ACM international conference on Information and knowledge management, pages 1633-1636. ACM, 2010.","title":"Understanding retweeting behaviors in social networks","authors":"Zi Yang, Jingyi Guo, Keke Cai, Jie Tang, Juanzi Li, Li Zhang, Zhong Su","year":"2010","venue":"Proceedings of the 19th ACM international conference on Information and knowledge management","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Yang et al., 2015] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. Network represen- tation learning with rich text information. In IJCAI, pages 2111-2117, 2015.","title":"Network representation learning with rich text information","authors":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Y Chang","year":"2015","venue":"IJCAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}