{"type":"ScholarlyArticle","title":"Deep Active Learning for Anchor User Prediction","authors":[{"name":"Anfeng Cheng","slug":"anfeng-cheng","given":"Anfeng","family":"Cheng","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; School of Cyber Security , University of Chinese Academy of Sciences , Beijing , China"},{"name":"Chuan Zhou","slug":"chuan-zhou","given":"Chuan","family":"Zhou","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; School of Cyber Security , University of Chinese Academy of Sciences , Beijing , China"},{"name":"Hong Yang","slug":"hong-yang","given":"Hong","family":"Yang","affiliation":"Centre for Artificial Intelligence , School of Software , FEIT , University of Technology Sydney"},{"name":"Jia Wu","slug":"jia-wu","given":"Jia","family":"Wu","affiliation":"Department of Computing , Macquarie University , Sydney , Australia"},{"name":"Lei Li","slug":"lei-li","given":"Lei","family":"Li","affiliation":"School of Computer Science and Information Engineering , Hefei University of Technology , China"},{"name":"Jianlong Tan","slug":"jianlong-tan","given":"Jianlong","family":"Tan","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; School of Cyber Security , University of Chinese Academy of Sciences , Beijing , China"},{"name":"Li Guo","slug":"li-guo","given":"Li","family":"Guo","affiliation":"Institute of Information Engineering , Chinese Academy of Sciences , Beijing , China; School of Cyber Security , University of Chinese Academy of Sciences , Beijing , China"}],"abstract":"Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction. To this end, we present a deep active learning model for anchor user prediction (DALAUP for short). However, active learning for anchor user sampling meets the challenges of non-i.i.d. user pair data caused by network structures and the correlation among anchor or non-anchor user pairs. To solve the challenges, DALAUP uses a couple of neural networks with shared-parameter to obtain the vector representations of user pairs, and ensembles three query strategies to select the most informative user pairs for labeling and model training. Experiments on real-world social network data demonstrate that DALAUP outperforms the state-of-the-art approaches.","doi":"10.24963/ijcai.2019/298","url":"https://ijcai.org/proceedings/2019/298","pdf":"https://ijcai.org/proceedings/2019/0298.pdf","markdown":"https://ijcai.org/proceedings/2019/298.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/298","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":298,"pages":{"first":2151,"last":2157},"keywords":["Machine Learning: Active Learning","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal et al., 2014] Charu C Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and Philip S Yu. Active learn- ing: A survey. 2014.","title":"Active learning: A survey","authors":"Xiangnan Charu C Aggarwal, Quanquan Kong, Jiawei Gu, Philip S Han, Yu","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Auer et al., 2002] Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, and Robert E Schapire. The nonstochastic mul- tiarmed bandit problem. SIAM journal on computing, 32(1):48-77, 2002.","title":"The nonstochastic multiarmed bandit problem","authors":"Peter Auer, Nicolo Cesa-Bianchi, Yoav Freund, Robert E Schapire","year":"2002","venue":"SIAM journal on computing","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Japkowicz and Stephen, 2002] Nathalie Japkowicz and Shaju Stephen. The class imbalance problem: A sys- tematic study. Intelligent data analysis, 6(5):429-449, 2002.","title":"The class imbalance problem: A systematic study","authors":"Stephen ; Japkowicz, Nathalie Japkowicz, Shaju Stephen","year":"2002","venue":"Intelligent data analysis","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Klau, 2009] Gunnar W Klau. A new graph-based method for pairwise global network alignment. BMC bioinformat- ics, 10(1):S59, 2009.","title":"A new graph-based method for pairwise global network alignment","authors":"W Gunnar, Klau","year":"2009","venue":"BMC bioinformatics","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Kollias et al., 2012] Giorgos Kollias, Shahin Mohammadi, and Ananth Grama. Network similarity decomposition (nsd): A fast and scalable approach to network alignment. IEEE Transactions on Knowledge and Data Engineering, 24(12):2232-2243, 2012.","title":"Network similarity decomposition (nsd): A fast and scalable approach to network alignment","authors":"Giorgos Kollias, Shahin Mohammadi, Ananth Grama","year":"2012","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Kong et al., 2013] Xiangnan Kong, Jiawei Zhang, and Philip S Yu. Inferring anchor links across multiple het- erogeneous social networks. In Proceedings of the 22nd ACM international conference on Information & Knowl- edge Management, pages 179-188. ACM, 2013.","title":"Inferring anchor links across multiple heterogeneous social networks","authors":"Kong","year":"2013","venue":"Proceedings of the 22nd ACM international conference on Information & Knowledge Management","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Li and Lin, 2014] Chung-Yi Li and Shou-De Lin. Match- ing users and items across domains to improve the rec- ommendation quality. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discov- ery and data mining, pages 801-810. ACM, 2014.","title":"Matching users and items across domains to improve the recommendation quality","authors":"Chung-Yi Li, Shou-De Lin","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":8,"raw":"[Liu et al., 2013] Jing Liu, Fan Zhang, Xinying Song, Young-In Song, Chin-Yew Lin, and Hsiao-Wuen Hon. What's in a name?: an unsupervised approach to link users across communities. In Proceedings of the sixth ACM in- ternational conference on Web search and data mining, pages 495-504. ACM, 2013.","title":"What's in a name?: an unsupervised approach to link users across communities","authors":"Jing Liu, Fan Zhang, Xinying Song, Young-In Song, Chin-Yew Lin, Hsiao-Wuen Hon","year":"2013","venue":"Proceedings of the sixth ACM international conference on Web search and data mining","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu et al., 2016] Li Liu, William K Cheung, Xin Li, and Lejian Liao. Aligning users across social networks using network embedding. In IJCAI, pages 1774-1780, 2016.","title":"Aligning users across social networks using network embedding","authors":"Li Liu, William K Cheung, Xin Li, Lejian Liao","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/254"},{"position":10,"raw":"[Man et al., 2016] Tong Man, Huawei Shen, Shenghua Liu, Xiaolong Jin, and Xueqi Cheng. Predict anchor links across social networks via an embedding approach. In IJ- CAI, volume 16, pages 1823-1829, 2016.","title":"Predict anchor links across social networks via an embedding approach","authors":"Huawei Tong Man, Shenghua Shen, Xiaolong Liu, Xueqi Jin, Cheng","year":"2016","venue":"IJ-CAI","doi":null,"url":null,"internal_url":"/proceedings/2016/261"},{"position":11,"raw":"[Niepert et al., 2016] Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. Learning convolutional neural networks for graphs. In International conference on ma- chine learning, pages 2014-2023, 2016.","title":"Learning convolutional neural networks for graphs","authors":"Mathias Niepert, Mohamed Ahmed, Konstantin Kutzkov","year":"2016","venue":"International conference on machine learning","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Peng et al., 2013] Chuan Peng, Kuai Xu, Feng Wang, and Haiyan Wang. Predicting information diffusion initiated from multiple sources in online social networks. In 2013 Sixth International Symposium on Computational Intelli- gence and Design, volume 2, pages 96-99. IEEE, 2013.","title":"Predicting information diffusion initiated from multiple sources in online social networks","authors":"Chuan Peng, Kuai Xu, Feng Wang, Haiyan Wang","year":"2013","venue":"Sixth International Symposium on Computational Intelligence and Design","doi":null,"url":null,"internal_url":null},{"position":13,"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":14,"raw":"[Riederer et al., 2016] Christopher Riederer, Yunsung Kim, Augustin Chaintreau, Nitish Korula, and Silvio Lattanzi. Linking users across domains with location data: Theory and validation. In Proceedings of the 25th International Conference on World Wide Web, pages 707-719. Interna- tional World Wide Web Conferences Steering Committee, 2016.","title":"Linking users across domains with location data: Theory and validation","authors":"Christopher Riederer, Yunsung Kim, Augustin Chaintreau, Nitish Korula, Silvio Lattanzi","year":"2016","venue":"Proceedings of the 25th International Conference on World Wide Web","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Settles, 2009] Burr Settles. Active learning literature sur- vey. University of Wisconsinmadison, 39(2):12-C131, 2009.","title":"Burr Settles. Active learning literature survey","authors":"Settles","year":"2009","venue":"University of Wisconsinmadison","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shen et al., 2004] Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, and Chew-Lim Tan. Multi-criteria-based active learning for named entity recognition. In Proceedings of the 42nd Annual Meeting on Association for Compu- tational Linguistics, page 589. Association for Computa- tional Linguistics, 2004.","title":"Multi-criteria-based active learning for named entity recognition","authors":"Dan Shen, Jie Zhang, Jian Su, Guodong Zhou, Chew-Lim Tan","year":"2004","venue":"Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shu et al., 2017] Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, and Huan Liu. User identity linkage across on- line social networks: A review. Acm Sigkdd Explorations Newsletter, 18(2):5-17, 2017.","title":"User identity linkage across online social networks: A review","authors":"Kai Shu, Suhang Wang, Jiliang Tang, Reza Zafarani, Huan Liu","year":"2017","venue":"Acm Sigkdd Explorations Newsletter","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Singh et al., 2007] Rohit Singh, Jinbo Xu, and Bonnie Berger. Pairwise global alignment of protein interaction networks by matching neighborhood topology. In Annual International Conference on Research in Computational Molecular Biology, pages 16-31. Springer, 2007.","title":"Pairwise global alignment of protein interaction networks by matching neighborhood topology","authors":"Rohit Singh, Jinbo Xu, Bonnie Berger","year":"2007","venue":"Annual International Conference on Research in Computational Molecular Biology","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Tan et al., 2014] Shulong Tan, Ziyu Guan, Deng Cai, Xuzhen Qin, Jiajun Bu, and Chun Chen. Mapping users across networks by manifold alignment on hypergraph. In Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.","title":"Mapping users across networks by manifold alignment on hypergraph","authors":"Shulong Tan, Ziyu Guan, Deng Cai, Xuzhen Qin, Jiajun Bu, Chun Chen","year":"2014","venue":"Twenty-Eighth AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tong et al., 2006] Hanghang Tong, Christos Faloutsos, and Jia-Yu Pan. Fast random walk with restart and its applica- tions. In Sixth International Conference on Data Mining (ICDM'06), pages 613-622. IEEE, 2006.","title":"Fast random walk with restart and its applications","authors":"Tong","year":"2006","venue":"Sixth International Conference on Data Mining (ICDM'06)","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Zhang et al., 2013] Jiawei Zhang, Xiangnan Kong, and S Yu Philip. Predicting social links for new users across aligned heterogeneous social networks. In 2013 IEEE 13th International Conference on Data Mining, pages 1289- 1294. IEEE, 2013.","title":"Predicting social links for new users across aligned heterogeneous social networks","authors":"Jiawei Zhang, Xiangnan Kong, S Yu, Philip","year":"2013","venue":"IEEE 13th International Conference on Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}