{"type":"ScholarlyArticle","title":"Network Embedding under Partial Monitoring for Evolving Networks","authors":[{"name":"Yu Han","slug":"yu-han","given":"Yu","family":"Han","affiliation":"Tsinghua University"},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":"Tsinghua University"},{"name":"Qian Chen","slug":"qian-chen","given":"Qian","family":"Chen","affiliation":"Tencent Technology (SZ) Co. , Ltd."}],"abstract":"Network embedding has been extensively studied in recent years. In addition to the works on static networks, some researchers try to propose new models for evolving networks. However, sometimes most of these dynamic network embedding models are still not in line with the actual situation, since these models have a strong assumption that we can achieve all the changes in the whole network, while in fact we cannot do this in some real world networks, such as the web networks and some large social networks. So in this paper, we study a novel and challenging problem, i.e., network embedding under partial monitoring for evolving networks. We propose a model on dynamic networks in which we cannot perceive all the changes of the structure. We analyze our model theoretically, and give a bound to the error between the results of our model and the potential optimal cases. We evaluate the performance of our model from two aspects. The experimental results on real world datasets show that our model outperforms the baseline models by a large margin.","doi":"10.24963/ijcai.2019/342","url":"https://ijcai.org/proceedings/2019/342","pdf":"https://ijcai.org/proceedings/2019/0342.pdf","markdown":"https://ijcai.org/proceedings/2019/342.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/342","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":342,"pages":{"first":2463,"last":2469},"keywords":["Machine Learning Applications: Applications of Reinforcement Learning","Machine Learning Applications: Networks","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Adamic and Adar, 2003] Lada A Adamic and Eytan Adar. Friends and neighbors on the web. Social networks, 25(3):211-230, 2003.","title":"Friends and neighbors on the web","authors":"A Lada, Eytan Adamic, Adar","year":"2003","venue":"Social networks","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Afriat, 1957] Sydney N Afriat. Orthogonal and oblique projectors and the characteristics of pairs of vector spaces. In Mathematical Proceedings of the Cambridge Philosophical Society, volume 53, pages 800-816. Cambridge Univ Press, 1957.","title":"Orthogonal and oblique projectors and the characteristics of pairs of vector spaces","authors":"Sydney N Afriat, Afriat","year":"1957","venue":"Mathematical Proceedings of the Cambridge Philosophical Society","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Anagnostopoulos et al., 2012] Aris Anagnostopoulos, Ravi Ku- mar, Mohammad Mahdian, Eli Upfal, and Fabio Vandin. Al- gorithms on evolving graphs. In Proceedings of the 3rd Innova- tions in Theoretical Computer Science Conference, pages 149- 160. ACM, 2012.","title":"Algorithms on evolving graphs","authors":"Aris Anagnostopoulos, Ravi Kumar, Mohammad Mahdian, Eli Upfal, Fabio Vandin","year":"2012","venue":"Proceedings of the 3rd Innovations in Theoretical Computer Science Conference","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Auer et al., 2002] Peter Auer, Nicolo Cesa-Bianchi, and Paul Fis- cher. Finite-time analysis of the multiarmed bandit problem. Ma- chine learning, 47(2-3):235-256, 2002.","title":"Finite-time analysis of the multiarmed bandit problem","authors":"Peter Auer, Nicolo Cesa-Bianchi, Paul Fischer","year":"2002","venue":"Machine learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bahmani et al., 2012] Bahman Bahmani, Ravi Kumar, Moham- mad Mahdian, and Eli Upfal. Pagerank on an evolving graph. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 24-32. ACM, 2012.","title":"Pagerank on an evolving graph","authors":"Bahman Bahmani, Ravi Kumar, Mohammad Mahdian, Eli Upfal","year":"2012","venue":"Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Barabâsi et al., 2002] Albert-Laszlo Barabâsi, Hawoong Jeong, Zoltan Néda, Erzsebet Ravasz, Andras Schubert, and Tamas Vic- sek. Evolution of the social network of scientific collabora- tions. Physica A: Statistical mechanics and its applications, 311(3):590-614, 2002.","title":"Evolution of the social network of scientific collaborations","authors":"Albert-Laszlo Barabâsi, Hawoong Jeong, Zoltan Néda, Erzsebet Ravasz, Andras Schubert, Tamas Vicsek","year":"2002","venue":"Physica A: Statistical mechanics and its applications","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Benson et al., 2016] Austin R Benson, David F Gleich, and Jure Leskovec. Higher-order organization of complex networks. Sci- ence, 353(6295):163-166, 2016.","title":"Higher-order organization of complex networks","authors":"David F Austin R Benson, Jure Gleich, Leskovec","year":"2016","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai Xu. Grarep: Learning graph representations with global structural in- formation. In CIKM, pages 891-900. ACM, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"CIKM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Chen et al., 2013] Wei Chen, Yajun Wang, and Yang Yuan. Com- binatorial multi-armed bandit: General framework, results and applications. In Proceedings of the 30th ICML, pages 151-159, 2013.","title":"Combinatorial multi-armed bandit: General framework, results and applications","authors":"Wei Chen, Yajun Wang, Yang Yuan","year":"2013","venue":"Proceedings of the 30th ICML","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Garivier and Cappé, 2011] Aurélien Garivier and Olivier Cappé. The kl-ucb algorithm for bounded stochastic bandits and beyond. In COLT, pages 359-376, 2011.","title":"Aurélien Garivier and Olivier Cappé. The kl-ucb algorithm for bounded stochastic bandits and beyond","authors":"Cappé Garivier","year":"2011","venue":"COLT","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceed- ings of the 22nd ACM SIGKDD, 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","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Han and Tang, 2017] Yu Han and Jie Tang. Who to invite next? predicting invitees of social groups. In IJCAI, pages 3714-3720, 2017.","title":"Who to invite next? predicting invitees of social groups","authors":"Yu Han, Jie Tang","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/519"},{"position":13,"raw":"[Jaccard, 1912] Paul Jaccard. The distribution of the flora in the alpine zone. 1. New phytologist, 11(2):37-50, 1912.","title":"The distribution of the flora in the alpine zone. 1","authors":"Jaccard","year":"1912","venue":"New phytologist","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Jeh and Widom, 2002] Glen Jeh and Jennifer Widom. Simrank: a measure of structural-context similarity. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 538-543. ACM, 2002.","title":"Simrank: a measure of structural-context similarity","authors":"Widom Jeh, Glen Jeh, Jennifer Widom","year":"2002","venue":"Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Katz, 1953] Leo Katz. A new status index derived from sociomet- ric analysis. Psychometrika, 18(1):39-43, 1953.","title":"A new status index derived from sociometric analysis","authors":"Leo Katz, Katz","year":"1953","venue":"Psychometrika","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Koren et al., 2009] Yehuda Koren, Robert Bell, and Chris Volin- sky. Matrix factorization techniques for recommender systems. Computer, 42(8), 2009.","title":"Matrix factorization techniques for recommender systems","authors":"Koren","year":"2009","venue":"Computer","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Leskovec et al., 2005] Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graphs over time: densification laws, shrinking diam- eters and possible explanations. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining, pages 177-187. ACM, 2005.","title":"Graphs over time: densification laws, shrinking diameters and possible explanations","authors":"Jure Leskovec, Jon Kleinberg, Christos Faloutsos","year":"2005","venue":"Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery and data mining","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Levy and Goldberg, 2014] Omer Levy and Yoav Goldberg. Neural word embedding as implicit matrix factorization. In Advances in neural information processing systems, pages 2177-2185, 2014.","title":"Neural word embedding as implicit matrix factorization","authors":"Goldberg Levy, Omer Levy, Yoav Goldberg","year":"2014","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Mirsky, 1960] Leon Mirsky. Symmetric gauge functions and uni- tarily invariant norms. The quarterly journal of mathematics, 11:50-59, 1960.","title":"Symmetric gauge functions and unitarily invariant norms","authors":"Leon Mirsky","year":"1960","venue":"The quarterly journal of mathematics","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social representations. In Proceedings of the 20th ACM SIGKDD, 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","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Qiu et al., 2017] Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network embedding as matrix factorization: Unifyingdeepwalk, line, pte, and node2vec. arXiv preprint arXiv:1710.02971, 2017.","title":"Network embedding as matrix factorization: Unifyingdeepwalk, line, pte, and node2vec","authors":"Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Song et al., 2009] Han Hee Song, Tae Won Cho, Vacha Dave, Yin Zhang, and Lili Qiu. Scalable proximity estimation and link pre- diction in online social networks. In Proceedings of the 9th ACM SIGCOMM, pages 322-335. ACM, 2009.","title":"Scalable proximity estimation and link prediction in online social networks","authors":"Hee Han, Tae Won Song, Vacha Cho, Yin Dave, Lili Zhang, Qiu","year":"2009","venue":"Proceedings of the 9th ACM SIGCOMM","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Stewart, 1990] Gilbert W Stewart. Matrix perturbation theory. 1990.","title":"Matrix perturbation theory","authors":"Gilbert W Stewart","year":"1990","venue":null,"doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Tang and Liu, 2009] Lei Tang and Huan Liu. Relational learning via latent social dimensions. In Proceedings of the 15th ACM SIGKDD, pages 817-826. ACM, 2009.","title":"Relational learning via latent social dimensions","authors":"Liu ; ] Lei Tang, Huan Tang, Liu","year":"2009","venue":"Proceedings of the 15th ACM SIGKDD","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large-scale informa- tion network embedding. In Proceedings of the 24th Interna- tional Conference on World Wide Web, pages 1067-1077. ACM, 2015.","title":"Line: Large-scale 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":27,"raw":"[Tong et al., 2008] Hanghang Tong, Spiros Papadimitriou, Philip S Yu, and Christos Faloutsos. Proximity tracking on time-evolving bipartite graphs. In Proceedings of the 2008 SIAM International Conference on Data Mining, pages 704-715. SIAM, 2008.","title":"Proximity tracking on time-evolving bipartite graphs","authors":"Hanghang Tong, Spiros Papadimitriou, Philip S Yu, Christos Faloutsos","year":"2008","venue":"Proceedings of the 2008 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Tu et al., 2016] Cunchao Tu, Hao Wang, Xiangkai Zeng, Zhiyuan Liu, and Maosong Sun. Community-enhanced network rep- resentation learning for network analysis. arXiv preprint arXiv:1611.06645, 2016.","title":"Community-enhanced network representation learning for network analysis","authors":"Cunchao Tu, Hao Wang, Xiangkai Zeng, Zhiyuan Liu, Maosong Sun","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2017] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. Community preserving net- work embedding. 2017.","title":"Community preserving network embedding","authors":"Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wedin, 1972] Per-Åke Wedin. Perturbation bounds in connection with singular value decomposition. BIT Numerical Mathematics, 12(1):99-111, 1972.","title":"Per-Åke Wedin. Perturbation bounds in connection with singular value decomposition","authors":"Wedin","year":"1972","venue":"BIT Numerical Mathematics","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Zheng et al., 2016] Vincent W Zheng, Sandro Cavallari, Hongyun Cai, Kevin Chen-Chuan Chang, and Erik Cambria. From node embedding to community embedding. arXiv preprint arXiv:1610.09950, 2016.","title":"From node embedding to community embedding","authors":"Sandro Vincent W Zheng, Hongyun Cavallari, Kevin Cai, Chang Chen-Chuan, Erik Cambria","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhou et al., 2018] Le-kui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. Dynamic network embedding by modeling triadic closure process. In AAAI, 2018.","title":"Dynamic network embedding by modeling triadic closure process","authors":"Le-Kui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Zhu et al., 2016] Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, and Aram Galstyan. Scalable temporal latent space inference for link prediction in dynamic social net- works. IEEE Transactions on Knowledge and Data Engineering, 28(10):2765-2777, 2016.","title":"Scalable temporal latent space inference for link prediction in dynamic social networks","authors":"Linhong Zhu, Dong Guo, Junming Yin, Greg Ver Steeg, Aram Galstyan","year":"2016","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}