{"type":"ScholarlyArticle","title":"Motif-Preserving Temporal Network Embedding","authors":[{"name":"Hong Huang","slug":"hong-huang","given":"Hong","family":"Huang","affiliation":"National Engineering Research Center for Big Data Technology and System; Service Computing Technology and Systems Laboratory; School of Computer Science and Technology , Huazhong University of Science and Technology , China"},{"name":"Zixuan Fang","slug":"zixuan-fang","given":"Zixuan","family":"Fang","affiliation":"National Engineering Research Center for Big Data Technology and System; Service Computing Technology and Systems Laboratory; School of Computer Science and Technology , Huazhong University of Science and Technology , China"},{"name":"Xiao Wang","slug":"xiao-wang","given":"Xiao","family":"Wang","affiliation":"Beijing University of Posts and Telecommunications , China"},{"name":"Youshan Miao","slug":"youshan-miao","given":"Youshan","family":"Miao","affiliation":"Microsoft Research Asia"},{"name":"Hai Jin","slug":"hai-jin","given":"Hai","family":"Jin","affiliation":"National Engineering Research Center for Big Data Technology and System; Service Computing Technology and Systems Laboratory; School of Computer Science and Technology , Huazhong University of Science and Technology , China"}],"abstract":"Network embedding, mapping nodes in a network to a low-dimensional space, achieves powerful performance. An increasing number of works focus on static network embedding, however, seldom attention has been paid to temporal network embedding, especially without considering the effect of mesoscopic dynamics when the network evolves. In light of this, we concentrate on a particular motif --- triad --- and its temporal dynamics, to study the temporal network embedding. Specifically, we propose MTNE, a novel embedding model for temporal networks. MTNE not only integrates the Hawkes process to stimulate the triad evolution process that preserves motif-aware high-order proximities, but also combines attention mechanism to distinguish the importance of different types of triads better. Experiments on various real-world temporal networks demonstrate that, compared with several state-of-the-art methods, our model achieves the best performance in both static and dynamic tasks, including node classification, link prediction,  and link recommendation.","doi":"10.24963/ijcai.2020/172","url":"https://ijcai.org/proceedings/2020/172","pdf":"https://ijcai.org/proceedings/2020/0172.pdf","markdown":"https://ijcai.org/proceedings/2020/172.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/172","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Data Mining","track":"Main track","number":172,"pages":{"first":1237,"last":1243},"keywords":["Data Mining: Mining Spatial, Temporal Data","Data Mining: Mining Graphs, Semi Structured Data, Complex Data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24524/file_24524.m3u8","length_sec":264,"provider":"hls"}],"references":[{"position":1,"raw":"[Bahdanau et al., 2015] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. In Proceedings of ICLR '15, 2015.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2015","venue":"Proceedings of ICLR '15","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 CIKM '15, pages 891-900. ACM, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"Proceedings of CIKM '15","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cui et al., 2018] Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. A survey on network embedding. IEEE TKDE, 31(5):833-852, 2018.","title":"A survey on network embedding","authors":"Peng Cui, Xiao Wang, Jian Pei, Wenwu Zhu","year":"2018","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Du et al., 2018] Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang. Dynamic network embedding: An extended approach for skip-gram based network em- bedding. In Proceedings of IJCAI '18, pages 2086-2092, 2018.","title":"Dynamic network embedding: An extended approach for skip-gram based network embedding","authors":"Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang","year":"2018","venue":"Proceedings of IJCAI '18","doi":null,"url":null,"internal_url":"/proceedings/2018/288"},{"position":5,"raw":"[Fournet and Barrat, 2014] Julie Fournet and Alain Barrat. Contact patterns among high school students. PloS one, 9(9), 2014.","title":"Contact patterns among high school students","authors":"Barrat Fournet, Julie Fournet, Alain Barrat","year":"2014","venue":"PloS one","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceedings of KDD '16, pages 855-864. ACM, 2016.","title":"node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of KDD '16","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Hawkes, 1971] Alan G. Hawkes. Spectra of some self-exciting and mutually exciting point processes. Biometrika, 58(1):83-90, 1971.","title":"Spectra of some self-exciting and mutually exciting point processes","authors":"Alan G Hawkes","year":"1971","venue":"Biometrika","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hogg and Lerman, 2012] Tad Hogg and Kristina Lerman. Social dynamics of digg. EPJ Data Science, 1(1):5, 2012.","title":"Social dynamics of digg","authors":"Lerman Hogg, Tad Hogg, Kristina Lerman","year":"2012","venue":"EPJ Data Science","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Huang et al., 2015] Hong Huang, Jie Tang, Lu Liu, JarDer Luo, and Xiaoming Fu. Triadic closure pattern anal- ysis and prediction in social networks. IEEE TKDE, 27(12):3374-3389, 2015.","title":"Triadic closure pattern analysis and prediction in social networks","authors":"Huang","year":"2015","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huang et al., 2018] Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, and Xiaoming Fu. Will triadic closure strengthen ties in social networks? TKDD, 12(3):30, 2018.","title":"Will triadic closure strengthen ties in social networks","authors":"Huang","year":"2018","venue":"TKDD","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ley, 2009] Michael Ley. Dblp -some lessons learned. Pro- ceedings of the VLDB Endowment, 2(2):1493-1500, 2009.","title":"Dblp -some lessons learned","authors":"Michael Ley","year":"2009","venue":"Proceedings of the VLDB Endowment","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2017] Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. Attributed network em- bedding for learning in a dynamic environment. In Pro- ceedings of CIKM '17, page 387-396, 2017.","title":"Attributed network embedding for learning in a dynamic environment","authors":"Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, Huan Liu","year":"2017","venue":"Proceedings of CIKM '17","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lu et al., 2019] Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S Yu, and Yanfang Ye. Temporal network embedding with micro-and macro-dynamics. In Proceedings of CIKM '19, pages 469-478. ACM, 2019.","title":"Temporal network embedding with micro-and macro-dynamics","authors":"Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S Yu, Yanfang Ye","year":"2019","venue":"Proceedings of CIKM '19","doi":null,"url":null,"internal_url":null},{"position":14,"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 '13, pages 3111-3119, 2013.","title":"Distributed representations of words and phrases and their compositionality","authors":"Mikolov","year":"2013","venue":"Proceedings of NIPS '13","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Nguyen et al., 2018] Giang Hoang Nguyen, John Boaz Lee, Ryan A. Rossi, Nesreen K. Ahmed, Eunyee Koh, and Sungchul Kim. Continuous-time dynamic network em- beddings. In Proceedings of the Web Conference 2018, pages 969-976, 2018.","title":"Continuous-time dynamic network embeddings","authors":"Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, Sungchul Kim","year":"2018","venue":"Proceedings of the Web Conference 2018","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social rep- resentations. In Proceedings of KDD '14, 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 KDD '14","doi":null,"url":null,"internal_url":null},{"position":17,"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 WWW '15, pages 1067-1077, 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 WWW '15","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wang et al., 2017] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. Community pre- serving network embedding. In Proceedings of AAAI '17, page 203-209, 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 '17","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Waters, 2015] Tony Waters. Weber's rationalism and mod- ern society: New translations on politics, bureaucracy, and social stratification. Springer, 2015.","title":"Weber's rationalism and modern society: New translations on politics, bureaucracy, and social stratification","authors":"Tony Waters","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yu et al., 2018] Wenchao Yu, Wei Cheng, Charu C. Aggar- wal, Kai Zhang, Haifeng Chen, and Wei Wang. Netwalk: A flexible deep embedding approach for anomaly detec- tion in dynamic networks. In Proceedings of KDD '18, page 2672-2681, 2018.","title":"Netwalk: A flexible deep embedding approach for anomaly detection in dynamic networks","authors":"Wenchao Yu, Wei Cheng, Charu C Aggarwal, Kai Zhang, Haifeng Chen, Wei Wang","year":"2018","venue":"Proceedings of KDD '18","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yu et al., 2019] Yanlei Yu, Zhiwu Lu, Jiajun Liu, Guop- ing Zhao, Ji-Rong Wen, and Kai Zheng. Rum: Network representation learning using motifs. In Proceedings of ICDE'19, pages 1382-1393, April 2019.","title":"Rum: Network representation learning using motifs","authors":"Yanlei Yu, Zhiwu Lu, Jiajun Liu, Guoping Zhao, Ji-Rong Wen, Kai Zheng","year":"2019","venue":"Proceedings of ICDE'19","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Zhang et al., 2013] Jing Zhang, Biao Liu, Jie Tang, Ting Chen, and Juanzi Li. Social influence locality for mod- eling retweeting behaviors. In Proceedings of IJCAI '13, page 2761-2767, 2013.","title":"Social influence locality for modeling retweeting behaviors","authors":"Jing Zhang, Biao Liu, Jie Tang, Ting Chen, Juanzi Li","year":"2013","venue":"Proceedings of IJCAI '13","doi":null,"url":null,"internal_url":"/proceedings/2013/406"},{"position":23,"raw":"[Zhou et al., 2018] Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. Dynamic network embedding by modeling triadic closure process. In Proceedings of AAAI '18, 2018.","title":"Dynamic network embedding by modeling triadic closure process","authors":"Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang","year":"2018","venue":"Proceedings of AAAI '18","doi":null,"url":null,"internal_url":null},{"position":24,"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 so- cial networks. IEEE TKDE, 28(10):2765-2777, 2016.","title":"Scalable temporal latent space inference for link prediction in dynamic social networks","authors":"Zhu","year":"2016","venue":"IEEE TKDE","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zhu et al., 2018] Dingyuan Zhu, Peng Cui, Ziwei Zhang, Jian Pei, and Wenwu Zhu. High-order proximity preserved embedding for dynamic networks. TKDE, 30(11):2134- 2144, Nov 2018.","title":"High-order proximity preserved embedding for dynamic networks","authors":"Dingyuan Zhu, Peng Cui, Ziwei Zhang, Jian Pei, Wenwu Zhu","year":"2018","venue":"TKDE","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zuo et al., 2018] Yuan Zuo, Guannan Liu, Hao Lin, Jia Guo, Xiaoqian Hu, and Junjie Wu. Embedding tempo- ral network via neighborhood formation. In Proceedings of KDD '18, pages 2857-2866. ACM, 2018.","title":"Embedding temporal network via neighborhood formation","authors":"Yuan Zuo, Guannan Liu, Hao Lin, Jia Guo, Xiaoqian Hu, Junjie Wu","year":"2018","venue":"Proceedings of KDD '18","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}