{"type":"ScholarlyArticle","title":"User Profile Preserving Social Network Embedding","authors":[{"name":"Daokun Zhang","slug":"daokun-zhang","given":"Daokun","family":"Zhang","affiliation":"a Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"},{"name":"Jie Yin","slug":"jie-yin","given":"Jie","family":"Yin","affiliation":"b Data61 , CSIRO , Australia"},{"name":"Xingquan Zhu","slug":"xingquan-zhu","given":"Xingquan","family":"Zhu","affiliation":"c Dept. of Computer & Electrical Engineering and Computer Science , Florida Atlantic University , USA; d School of Computer Science , Fudan University , China"},{"name":"Chengqi Zhang","slug":"chengqi-zhang","given":"Chengqi","family":"Zhang","affiliation":"a Centre for Artificial Intelligence , FEIT , University of Technology Sydney , Australia"}],"abstract":"This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent low-dimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPP-SNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPP-SNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.","doi":"10.24963/ijcai.2017/472","url":"https://ijcai.org/proceedings/2017/472","pdf":"https://ijcai.org/proceedings/2017/0472.pdf","markdown":"https://ijcai.org/proceedings/2017/472.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/472","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":472,"pages":{"first":3378,"last":3384},"keywords":["Machine Learning: Data Mining","Machine Learning: Unsupervised Learning","Machine Learning: Feature Selection/Construction"],"videos":[],"references":[{"position":1,"raw":"[Bhuyan et al., 2014] Monowar H Bhuyan, Dhruba Kumar Bhattacharyya, and Jugal K Kalita. Network anomaly de- tection: methods, systems and tools. IEEE Communica- tions Surveys & Tutorials, 16(1):303-336, 2014.","title":"Network anomaly detection: methods, systems and tools","authors":"Bhuyan","year":"2014","venue":"IEEE Communications Surveys & Tutorials","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":"[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":4,"raw":"[Gutmann and Hyvärinen, 2012] Michael U Gutmann and Aapo Hyvärinen. Noise-contrastive estimation of unnor- malized statistical models, with applications to natural im- age statistics. Journal of Machine Learning Research, 13(2):307-361, 2012.","title":"Noise-contrastive estimation of unnormalized statistical models, with applications to natural image statistics","authors":"Aapo Michael U Gutmann, Hyvärinen","year":"2012","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Huang et al., 2017] Xiao Huang, Jundong Li, and Xia Hu. Label informed attributed network embedding. In Pro- ceedings of 10th ACM International Conference on Web Search and Data Mining, pages 731-739, 2017.","title":"Label informed attributed network embedding","authors":"Xiao Huang, Jundong Li, Xia Hu","year":"2017","venue":"Proceedings of 10th ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Leskovec and Mcauley, 2012] Jure Leskovec and Julian J Mcauley. Learning to discover social circles in ego net- works. In Advances in Neural Information Processing Sys- tems, pages 539-547, 2012.","title":"Learning to discover social circles in ego networks","authors":"Jure Leskovec, Julian J Mcauley","year":"2012","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Lü and Zhou, 2011] Linyuan Lü and Tao Zhou. Link pre- diction in complex networks: A survey. Physica A: Statis- tical Mechanics and its Applications, 390(6):1150-1170, 2011.","title":"Linyuan Lü and Tao Zhou. Link prediction in complex networks: A survey","authors":"Zhou Lü","year":"2011","venue":"Physica A: Statistical Mechanics and its Applications","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed repre- sentations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems 26, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Pan et al., 2016] Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. Tri-party deep network representation. In Proceedings of the 25th International Joint Conference on Artificial Intelligence, pages 1895- 1901, 2016.","title":"Tri-party deep network representation","authors":"Pan","year":"2016","venue":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2016/271"},{"position":10,"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":11,"raw":"[Rahimi et al., 2007] Ali Rahimi, Benjamin Recht, et al. Random features for large-scale kernel machines. In Advances in Neural Information Processing Systems 20, pages 1177-1184, 2007.","title":"Random features for large-scale kernel machines","authors":"Ali Rahimi, Benjamin Recht","year":"2007","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Sen et al., 2008] Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. Collective classification in network data. AI Magazine, 29(3):93, 2008.","title":"Collective classification in network data","authors":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, Tina Eliassi-Rad","year":"2008","venue":"AI Magazine","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Strehl and Ghosh, 2003] A. Strehl and J. Ghosh. Cluster ensembles -a knowledge reuse framework for combin- ing multiple partitions. Journal of Machine Learning Re- search, 3:583-617, 2003.","title":"Cluster ensembles -a knowledge reuse framework for combining multiple partitions","authors":"Ghosh Strehl, A Strehl, J Ghosh","year":"2003","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":14,"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. ACM, 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":15,"raw":"[Traud et al., 2012] Amanda L Traud, Peter J Mucha, and Mason A Porter. Social structure of facebook networks. Physica A: Statistical Mechanics and its Applications, 391(16):4165-4180, 2012.","title":"Social structure of facebook networks","authors":"Amanda L Traud, Peter J Mucha, Mason A Porter","year":"2012","venue":"Physica A: Statistical Mechanics and its Applications","doi":null,"url":null,"internal_url":null},{"position":16,"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":17,"raw":"[Yang et al., 2013] Jaewon Yang, Julian McAuley, and Jure Leskovec. Community detection in networks with node attributes. In Proceedings of the 13th IEEE International Conference on Data Mining, pages 1151-1156. IEEE, 2013.","title":"Community detection in networks with node attributes","authors":"Jaewon Yang, Julian Mcauley, Jure Leskovec","year":"2013","venue":"Proceedings of the 13th IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":18,"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 Proceedings of the 24th International Joint Conference on Artificial In- telligence, 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":"Proceedings of the 24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/299"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}