{"type":"ScholarlyArticle","title":"Network-Specific Variational Auto-Encoder for Embedding in Attribute Networks","authors":[{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Bingyi Li","slug":"bingyi-li","given":"Bingyi","family":"Li","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Pengfei Jiao","slug":"pengfei-jiao","given":"Pengfei","family":"Jiao","affiliation":"Center of Biosafety Research and Strategy , Tianjin University , Tianjin , China; College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Dongxiao He","slug":"dongxiao-he","given":"Dongxiao","family":"He","affiliation":"College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Weixiong Zhang","slug":"weixiong-zhang","given":"Weixiong","family":"Zhang","affiliation":"Department of Computer Science and Engineering , Washington University , St. Louis , USA"}],"abstract":"Network embedding (NE) maps a network into a low-dimensional space while preserving intrinsic features of the network. Variational Auto-Encoder (VAE) has been actively studied for NE. These VAE-based methods typically utilize both network topologies and node semantics and treat these two types of data in the same way. However, the information of network topology and information of node semantics are orthogonal and are often from different sources; the former quantifies coupling relationships among nodes, whereas the latter represents node specific properties. Ignoring this difference affects NE. To address this issue, we develop a network-specific VAE for NE, named as NetVAE. In the encoding phase of our new approach, compression of network structures and compression of node attributes share the same encoder in order to perform co-training to achieve transfer learning and information integration. In the decoding phase, a dual decoder is introduced to reconstruct network topologies and node attributes separately. Specifically, as a part of the dual decoder, we develop a novel method based on a Gaussian mixture model and the block model to reconstruct network structures. Extensive experiments on large real-world networks demonstrate a superior performance of the new approach over the state-of-the-art methods.","doi":"10.24963/ijcai.2019/370","url":"https://ijcai.org/proceedings/2019/370","pdf":"https://ijcai.org/proceedings/2019/0370.pdf","markdown":"https://ijcai.org/proceedings/2019/370.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/370","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":370,"pages":{"first":2663,"last":2669},"keywords":["Machine Learning Applications: Networks","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Balasubramanyan and Cohen, 2011] Ramnath Balasub- ramanyan and William W Cohen. Block-lda: Jointly modeling entity-annotated text and entity-entity links. In Proceedings of the 2011 SIAM International Conference on Data Mining, pages 450-461, Vancouver,Canada, December 2011. SIAM.","title":"Block-lda: Jointly modeling entity-annotated text and entity-entity links","authors":"Ramnath Balasubramanyan, William W Cohen","year":"2011","venue":"Proceedings of the 2011 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cui et al., 2018] Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. A survey on network embedding. IEEE Transactions on Knowledge and Data Engineering, page 1, June 2018.","title":"A survey on network embedding","authors":"Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","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, California, USA, August 2016. ACM.","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":"[Holland et al., 1983] Paul W Holland, Kathryn Blackmond Laskey, and Samuel Leinhardt. Stochastic blockmodels: First steps. Social networks, 5(2):109-137, 1983.","title":"Stochastic blockmodels: First steps","authors":"Kathryn Blackmond Paul W Holland, Samuel Laskey, Leinhardt","year":"1983","venue":"Social networks","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 the 10th ACM International Conference on Web Search and Data Mining, pages 731-739, Cambridge, United Kingdom, February 2017. ACM.","title":"Label informed attributed network embedding","authors":"Xiao Huang, Jundong Li, Xia Hu","year":"2017","venue":"Proceedings of the 10th ACM International Conference on Web Search and Data Mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jin et al., 2018] Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, and Weixiong Zhang. Integrative network embedding via deep joint reconstruction. In Pro- ceedings of the 27th International Joint Conference on Ar- tificial Intelligence, pages 3407-3413, Stockhoim, Swe- den, July 2018.","title":"Integrative network embedding via deep joint reconstruction","authors":"Di Jin, Meng Ge, Liang Yang, Dongxiao He, Longbiao Wang, Weixiong Zhang","year":"2018","venue":"Proceedings of the 27th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/473"},{"position":7,"raw":"Morgan Kaufmann. [Karrer and Newman, 2011] Brian Karrer and Mark EJ New- man. Stochastic blockmodels and community structure in networks. Physical review E, 83(1):016107, January 2011.","title":"Stochastic blockmodels and community structure in networks","authors":"Morgan Kaufmann, . [ Karrer, Newman, Brian Karrer, Mark Ej Newman","year":"2011","venue":"Physical review E","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kingma and Welling, 2014] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. Stat, 1050(10):1, May 2014.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2014","venue":"Stat","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Variational graph auto-encoders. Stat, 1050:21, November 2016.","title":"Variational graph auto-encoders","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":"Stat","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Li et al., 2017] Hang Li, Haozheng Wang, Zhenglu Yang, and Haochen Liu. Effective representing of information network by variational autoencoder. In Proceedings of the 26th International Joint Conference on Artificial Intelli- gence, pages 2103-2109, Melbourne, Australia, August 2017.","title":"Effective representing of information network by variational autoencoder","authors":"Li","year":"2017","venue":"Proceedings of the 26th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2017/292"},{"position":11,"raw":"[Morgan Kaufmann. Liao et al., 2018] Lizi Liao, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. Attributed social network embedding. IEEE Transactions on Knowledge and Data Engineering, 30(12):2257-2270, 2018.","title":"Attributed social network embedding","authors":"Lizi Liao, Xiangnan He, Hanwang Zhang, Tat-Seng Chua","year":"2018","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Liu et al., 2012] Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, and Thomas S Huang. Constrained nonnega- tive matrix factorization for image representation. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 34(7):1299-1311, July 2012.","title":"Constrained nonnegative matrix factorization for image representation","authors":"Haifeng Liu, Zhaohui Wu, Xuelong Li, Deng Cai, Thomas S Huang","year":"2012","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Pan et al., 2016] Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Yang Wang. Tri-party deep network representation. Network, 11(9):12, February 2016.","title":"Tri-party deep network representation","authors":"Pan","year":"2016","venue":"Network","doi":null,"url":null,"internal_url":"/proceedings/2016/271"},{"position":14,"raw":"[Pan et al., 2019] Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. Adversarially reg- ularized graph autoencoder for graph embedding. arXiv preprint arXiv:1802.04407, page 1802.04407, January 2019.","title":"Adversarially regularized graph autoencoder for graph embedding","authors":"Pan","year":"2019","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2018/362"},{"position":15,"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, New York, USA, August 2014. ACM.","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":16,"raw":"[Tao et al., 2019] Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, and Yun Fu. Marginalized multiview en- semble clustering. IEEE transactions on neural networks and learning systems, pages 1-12, 2019.","title":"Marginalized multiview ensemble clustering","authors":"Zhiqiang Tao, Hongfu Liu, Sheng Li, Zhengming Ding, Yun Fu","year":"2019","venue":"IEEE transactions on neural networks and learning systems","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2016a] 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, California, USA, August 2016. ACM.","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":18,"raw":"[Wang et al., 2016b] Xiao Wang, Di Jin, Xiaochun Cao, Liang Yang, and Weixiong Zhang. Semantic community identification in large attribute networks. In 30th AAAI Conference on Artificial Intelligence, pages 2103-2109, Arizona, USA, February 2016. AAAI.","title":"Semantic community identification in large attribute networks","authors":"Wang","year":"2016","venue":"30th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Yang et al., 2015] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang. Network representa- tion learning with rich text information. In 24th Inter- national Joint Conference on Artificial Intelligence, pages 731-739, Arizona, USA, June 2015. ACM.","title":"Network representation learning with rich text information","authors":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang","year":"2015","venue":"24th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2015/299"},{"position":20,"raw":"[Yang et al., 2016] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Chang. Max-margin deepwalk: Discriminative learning of network representation. In Pro- ceedings of the 25th International Joint Conference on Ar- tificial Intelligence, pages 3889-3895, New York, USA, July 2016.","title":"Max-margin deepwalk: Discriminative learning of network representation","authors":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Chang","year":"2016","venue":"Proceedings of the 25th International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2016/547"},{"position":21,"raw":"[Morgan Kaufmann. Zhang et al., 2018a] Daokun Zhang, Jie Yin, Xingquan Zhu, and Chengqi Zhang. Network representation learn- ing: A survey. IEEE transactions on Big Data, pages 1 - 1, June 2018.","title":"Network representation learning: A survey","authors":"Morgan Kaufmann, Zhang","year":"2018","venue":"IEEE transactions on Big Data","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2018b] Ge Zhang, Di Jin, Jian Gao, Pengfei Jiao, Francoise Fogelman-Soulié, and Xin Huang. Find- ing communities with hierarchical semantics by distin- guishing general and specialized topics. In 30th AAAI Conference on Artificial Intelligence, pages 3648-3654, Louisiana, USA, February 2018. AAAI.","title":"Finding communities with hierarchical semantics by distinguishing general and specialized topics","authors":"Zhang","year":"2018","venue":"30th AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/507"},{"position":23,"raw":"[Zong et al., 2018] Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen. Deep autoencoding gaussian mixture model for unsupervised anomaly detection. February 2018.","title":"Deep autoencoding gaussian mixture model for unsupervised anomaly detection","authors":"Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, Haifeng Chen","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}