{"type":"ScholarlyArticle","title":"Fast Network Embedding Enhancement via High Order Proximity Approximation","authors":[{"name":"Cheng Yang","slug":"cheng-yang","given":"Cheng","family":"Yang","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China"},{"name":"Maosong Sun","slug":"maosong-sun","given":"Maosong","family":"Sun","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , Xuzhou , China"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , Xuzhou , China"},{"name":"Cunchao Tu","slug":"cunchao-tu","given":"Cunchao","family":"Tu","affiliation":"Department of Computer Science and Technology , Tsinghua University , Beijing , China; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , Xuzhou , China"}],"abstract":"Many Network Representation Learning (NRL) methods have been proposed to learn vector representations for vertices in a network recently. In this paper, we summarize most existing NRL methods into a unified two-step framework, including proximity matrix construction and dimension reduction. We focus on the analysis of proximity matrix construction step and conclude that an NRL method can be improved by exploring higher order proximities when building the proximity matrix. We propose Network Embedding Update (NEU) algorithm which implicitly approximates higher order proximities with theoretical approximation bound and can be applied on any NRL methods to enhance their performances. We conduct experiments on multi-label classification and link prediction tasks. Experimental results show that NEU can make a consistent and significant improvement over a number of NRL methods with almost negligible running time on all three publicly available datasets.","doi":"10.24963/ijcai.2017/544","url":"https://ijcai.org/proceedings/2017/544","pdf":"https://ijcai.org/proceedings/2017/0544.pdf","markdown":"https://ijcai.org/proceedings/2017/544.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/544","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":"Multidisciplinary Topics and Applications","track":"Main track","number":544,"pages":{"first":3894,"last":3900},"keywords":["Multidisciplinary Topics and Applications: AI and Social Sciences","Machine Learning: Unsupervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Akoglu et al., 2015] Leman Akoglu, Hanghang Tong, and Danai Koutra. Graph based anomaly detection and de- scription: a survey. Data Mining and Knowledge Discov- ery, 2015.","title":"Graph based anomaly detection and description: a survey","authors":"Leman Akoglu, Hanghang Tong, Danai Koutra","year":"2015","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Ben-Hur and Weston, 2010] Asa Ben-Hur and Jason West- on. A users guide to support vector machines. Data mining techniques for the life sciences, 2010.","title":"Asa Ben-Hur and Jason Weston. A users guide to support vector machines","authors":"Ben-Hur, Weston","year":"2010","venue":"Data mining techniques for the life sciences","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bengio et al., 2013] Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on PAMI, 2013.","title":"Representation learning: A review and new perspectives","authors":"Yoshua Bengio, Aaron Courville, Pascal Vincent","year":"2013","venue":"IEEE transactions on PAMI","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai X- u. Grarep: Learning graph representations with global structural information. In Proceedings of CIKM, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"Proceedings of CIKM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chang et al., 2015] Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, and Thomas S Huang. Heterogeneous network embedding via deep architectures. In Proceedings of KDD, 2015.","title":"Heterogeneous network embedding via deep architectures","authors":"Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C Aggarwal, Thomas S Huang","year":"2015","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fan et al., 2008] Rong-En Fan, Kai-Wei Chang, Cho-Jui H- sieh, Xiang-Rui Wang, and Chih-Jen Lin. Liblinear: A library for large linear classification. JMLR, 2008.","title":"Liblinear: A library for large linear classification","authors":"Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin","year":"2008","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hanley and McNeil, 1982] James A Hanley and Barbara J McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 1982.","title":"The meaning and use of the area under a receiver operating characteristic (roc) curve","authors":"A James, Barbara J Hanley, Mcneil","year":"1982","venue":"Radiology","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang and Mamoulis, 2017] Zhipeng Huang and Nikos Mamoulis. Heterogeneous information network embed- ding for meta path based proximity. arXiv preprint arX- iv:1701.05291, 2017.","title":"Heterogeneous information network embedding for meta path based proximity","authors":"Mamoulis Huang, Zhipeng Huang, Nikos Mamoulis","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2017] Xiao Huang, Jundong Li, and Xia Hu. Label informed attributed network embedding. In Pro- ceedings of WSDM, 2017.","title":"Label informed attributed network embedding","authors":"Xiao Huang, Jundong Li, Xia Hu","year":"2017","venue":"Proceedings of WSDM","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kipf and Welling, 2017] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR, 2017.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2017","venue":"Proceedings of ICLR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ley, 2002] Michael Ley. The dblp computer science bibli- ography: Evolution, research issues, perspectives. In In- ternational symposium on string processing and informa- tion retrieval, 2002.","title":"The dblp computer science bibliography: Evolution, research issues, perspectives. In International symposium on string processing and information retrieval","authors":"Michael Ley","year":"2002","venue":null,"doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2016] Juzheng Li, Jun Zhu, and Bo Zhang. Dis- criminative deep random walk for network classification. In Proceedings of ACL, 2016.","title":"Discriminative deep random walk for network classification","authors":"Li","year":"2016","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liben-Nowell and Kleinberg, 2007] David Liben-Nowell and Jon Kleinberg. The link-prediction problem for social networks. journal of the Association for Information Science and Technology, 2007.","title":"The link-prediction problem for social networks","authors":"Liben-Nowell, Kleinberg, David Liben-Nowell, Jon Kleinberg","year":"2007","venue":"journal of the Association for Information Science and Technology","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed rep- resentations of words and phrases and their composition- ality. In Proceedings of NIPS, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Proceedings of NIPS","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Ou et al., 2016] Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu. Asymmetric transitivity preserv- ing graph embedding. In Proceedings of KDD, 2016.","title":"Asymmetric transitivity preserving graph embedding","authors":"Ou","year":"2016","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In Proceedings of KDD, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Salton and McGill, 1986] Gerard Salton and Michael J M- cGill. Introduction to modern information retrieval. 1986.","title":"Introduction to modern information retrieval","authors":"Gerard Salton, J M-Cgill Michael","year":"1986","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sen et al., 2008] Prithviraj Sen, Galileo Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad. Collective classification in network data. AI Magazine, 2008.","title":"Collective classification in network data","authors":"Prithviraj Sen, Mark Galileo, Mustafa Namata, Lise Bilgic, Brian Getoor, Tina Gallagher, Eliassi-Rad","year":"2008","venue":"AI Magazine","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tang and Liu, 2009] Lei Tang and Huan Liu. Relational learning via latent social dimensions. In Proceedings of KDD, 2009.","title":"Relational learning via latent social dimensions","authors":"Liu ; ] Lei Tang, Huan Tang, Liu","year":"2009","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tang and Liu, 2011] Lei Tang and Huan Liu. Leveraging social media networks for classification. Proceedings of KDD, 2011.","title":"Leveraging social media networks for classification","authors":"Lei Tang, Huan Liu","year":"2011","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tang et al., 2015a] Jian Tang, Meng Qu, and Qiaozhu Mei. Pte: Predictive text embedding through large-scale hetero- geneous text networks. In Proceedings of KDD, 2015.","title":"Pte: Predictive text embedding through large-scale heterogeneous text networks","authors":"Tang","year":"2015","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tang et al., 2015b] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of WWW, 2015.","title":"Line: Largescale information network embedding","authors":"Tang","year":"2015","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Tu et al., 2014] Cunchao Tu, Zhiyuan Liu, and Maosong Sun. Inferring correspondences from multiple sources for microblog user tags. In Proceedings of SMP, 2014.","title":"Inferring correspondences from multiple sources for microblog user tags","authors":"Tu","year":"2014","venue":"Proceedings of SMP","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tu et al., 2016] Cunchao Tu, Weicheng Zhang, Zhiyuan Li- u, and Maosong Sun. Max-margin deepwalk: discrimina- tive learning of network representation. In Proceedings of IJCAI, 2016.","title":"Max-margin deepwalk: discriminative learning of network representation","authors":"Tu","year":"2016","venue":"Proceedings of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/547"},{"position":26,"raw":"[Wang et al., 2016] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In Proceedings of KDD, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2017] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. Community pre- serving network embedding. Proceedings of AAAI, 2017.","title":"Community preserving network embedding","authors":"Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang","year":"2017","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Xu et al., 2017] Linchuan Xu, Xiaokai Wei, Jiannong Cao, and Philip S Yu. Embedding of embedding (eoe): Joint embedding for coupled heterogeneous networks. In Pro- ceedings of WSDM, 2017.","title":"Embedding of embedding (eoe): Joint embedding for coupled heterogeneous networks","authors":"Xu","year":"2017","venue":"Proceedings of WSDM","doi":null,"url":null,"internal_url":null},{"position":29,"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 IJCAI, 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 IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/299"},{"position":30,"raw":"[Yang et al., 2016] Zhilin Yang, William Cohen, and Ruslan Salakhutdinov. Revisiting semi-supervised learning with graph embeddings. In Proceedings of ICML, 2016.","title":"Revisiting semi-supervised learning with graph embeddings","authors":"Zhilin Yang, William Cohen, Ruslan Salakhutdinov","year":"2016","venue":"Proceedings of ICML","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}