{"type":"ScholarlyArticle","title":"HID: Hierarchical Multiscale Representation Learning for Information Diffusion","authors":[{"name":"Honglu Zhou","slug":"honglu-zhou","given":"Honglu","family":"Zhou","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"},{"name":"Shuyuan Xu","slug":"shuyuan-xu","given":"Shuyuan","family":"Xu","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"},{"name":"Zuohui Fu","slug":"zuohui-fu","given":"Zuohui","family":"Fu","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"},{"name":"Gerard de Melo","slug":"gerard-de-melo","given":"Gerard","family":"de Melo","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"},{"name":"Yongfeng Zhang","slug":"yongfeng-zhang","given":"Yongfeng","family":"Zhang","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"},{"name":"Mubbasir Kapadia","slug":"mubbasir-kapadia","given":"Mubbasir","family":"Kapadia","affiliation":"Department of Computer Science , Rutgers University , New Brunswick , NJ 08901"}],"abstract":"Multiscale modeling has yielded immense success on various machine learning tasks. However, it has not been properly explored for the prominent task of information diffusion, which aims to understand how information propagates along users in online social networks. For a specific user, whether and when to adopt a piece of information propagated from another user is affected by complex interactions, and thus, is very challenging to model. Current state-of-the-art techniques invoke deep neural models with vector representations of users. In this paper, we present a Hierarchical Information Diffusion (HID) framework by integrating user representation learning and multiscale modeling. The proposed framework can be layered on top of all information diffusion techniques that leverage user representations, so as to boost the predictive power and learning efficiency of the original technique. Extensive experiments on three real-world datasets showcase the superiority of our method.","doi":"10.24963/ijcai.2020/468","url":"https://ijcai.org/proceedings/2020/468","pdf":"https://ijcai.org/proceedings/2020/0468.pdf","markdown":"https://ijcai.org/proceedings/2020/468.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/468","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":"Machine Learning Applications","track":"Main track","number":468,"pages":{"first":3385,"last":3391},"keywords":["Data Mining: Mining Text, Web, Social Media","Machine Learning Applications: Web Sciences","Data Mining: Mining Graphs, Semi Structured Data, Complex Data","Data Mining: Applications"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/26711/file_26711.m3u8","length_sec":844,"provider":"hls"}],"references":[{"position":1,"raw":"[Alber et al., 2019] Mark Alber, Adrian Buganza Tepole, William R Cannon, Suvranu De, Salvador Dura-Bernal, Krishna Garikipati, et al. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. npj Digital Medicine, 2019.","title":"Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences","authors":"Mark Alber, Adrian Buganza Tepole, Suvranu William R Cannon, Salvador De, Krishna Dura-Bernal, Garikipati","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bao et al., 2016] Qing Bao, William K Cheung, and Jiming Liu. Inferring motif-based diffusion models for social net- works. In IJCAI, pages 3677-3683, 2016.","title":"Inferring motif-based diffusion models for social networks","authors":"Qing Bao, William K Cheung, Jiming Liu","year":"2016","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/517"},{"position":3,"raw":"[Bourigault et al., 2014] Simon Bourigault, Cedric Lagnier, et al. Learning social network embeddings for predicting information diffusion. In WSDM. ACM, 2014.","title":"Learning social network embeddings for predicting information diffusion","authors":"Simon Bourigault, Cedric Lagnier","year":"2014","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bourigault et al., 2016] Simon Bourigault, Sylvain Lam- prier, and Patrick Gallinari. Representation learning for in- formation diffusion through social networks: an embedded cascade model. In WSDM, pages 573-582. ACM, 2016.","title":"Representation learning for information diffusion through social networks: an embedded cascade model","authors":"Simon Bourigault, Sylvain Lamprier, Patrick Gallinari","year":"2016","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chen et al., 2018] Haochen Chen, Bryan Perozzi, Yifan Hu, and Steven Skiena. Harp: Hierarchical representation learning for networks. In Proceedings of AAAI, 2018.","title":"Harp: Hierarchical representation learning for networks","authors":"Haochen Chen, Bryan Perozzi, Yifan Hu, Steven Skiena","year":"2018","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Feng et al., 2018] Shanshan Feng, Gao Cong, Arijit Khan, Xiucheng Li, Yong Liu, and Yeow Meng Chee. Inf2vec: Latent representation model for social influence embed- ding. In ICDE, pages 941-952. IEEE, 2018.","title":"Inf2vec: Latent representation model for social influence embedding","authors":"Shanshan Feng, Gao Cong, Arijit Khan, Xiucheng Li, Yong Liu, Yeow Meng Chee","year":"2018","venue":"ICDE","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gao et al., 2017] Sheng Gao, Huacan Pang, Patrick Galli- nari, et al. A novel embedding method for information dif- fusion prediction in social network big data. IEEE Trans- actions on Industrial Informatics, 13(4):2097-2105, 2017.","title":"A novel embedding method for information diffusion prediction in social network big data","authors":"Sheng Gao, Huacan Pang, Patrick Gallinari","year":"2017","venue":"IEEE Transactions on Industrial Informatics","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Guille et al., 2013] Adrien Guille, Hakim Hacid, Cecile Favre, et al. Information diffusion in online social net- works: A survey. ACM Sigmod Record, 42(2), 2013.","title":"Information diffusion in online social networks: A survey","authors":"Adrien Guille, Hakim Hacid, Cecile Favre","year":"2013","venue":"ACM Sigmod Record","doi":null,"url":null,"internal_url":null},{"position":9,"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":10,"raw":"[Kempe et al., 2003] David Kempe, Jon Kleinberg, and Éva Tardos. Maximizing the spread of influence through a so- cial network. In SIGKDD, pages 137-146. ACM, 2003.","title":"Maximizing the spread of influence through a social network","authors":"David Kempe, Jon Kleinberg, Éva Tardos","year":"2003","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lagnier et al., 2013] Cédric Lagnier, Ludovic Denoyer, et al. Predicting information diffusion in social networks using content and user's profiles. In ECIR, 2013.","title":"Predicting information diffusion in social networks using content and user's profiles","authors":"Cédric Lagnier, Ludovic Denoyer","year":"2013","venue":"ECIR","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Leskovec et al., 2007] Jure Leskovec, Lada A Adamic, and Bernardo A Huberman. The dynamics of viral marketing. ACM Transactions on the Web (TWEB), 1(1):5, 2007.","title":"The dynamics of viral marketing","authors":"Jure Leskovec, Lada A Adamic, Bernardo A Huberman","year":"2007","venue":"ACM Transactions on the Web (TWEB)","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Leskovec et al., 2009] Jure Leskovec, Lars Backstrom, and Jon Kleinberg. Meme-tracking and the dynamics of the news cycle. In SIGKDD, pages 497-506. ACM, 2009.","title":"Meme-tracking and the dynamics of the news cycle","authors":"Jure Leskovec, Lars Backstrom, Jon Kleinberg","year":"2009","venue":"SIGKDD","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Mano and Ishikawa, 2010] Masanori Mano and Yoshiharu Ishikawa. Anonymizing user location and profile informa- tion for privacy-aware mobile services. In SIGSPATIAL Workshop on LBSN. ACM, 2010.","title":"Anonymizing user location and profile information for privacy-aware mobile services","authors":"Ishikawa Mano, Masanori Mano, Yoshiharu Ishikawa","year":"2010","venue":"SIGSPATIAL Workshop on LBSN","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Meyes et al., 2019] Richard Meyes, Melanie Lu, Con- stantin Waubert de Puiseau, et al. Ablation studies in arti- ficial neural networks. CoRR, abs/1901.08644, 2019.","title":"Ablation studies in artificial neural networks","authors":"Richard Meyes, Melanie Lu, Constantin Waubert De Puiseau","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Morris et al., 2019] Christopher Morris, Martin Ritzert, et al. Weisfeiler and leman go neural: Higher-order graph neural networks. In AAAI, 2019.","title":"Weisfeiler and leman go neural: Higher-order graph neural networks","authors":"Morris","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Nadeau et al., 2008] Richard Nadeau, Neil Nevitte, Elisa- beth Gidengil, and André Blais. Election campaigns as information campaigns: who learns what and does it mat- ter? Political Communication, 25(3):229-248, 2008.","title":"Election campaigns as information campaigns: who learns what and does it matter?","authors":"Richard Nadeau, Neil Nevitte, Elisabeth Gidengil, André Blais","year":"2008","venue":"Political Communication","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Perozzi et al., 2017] Bryan Perozzi, Vivek Kulkarni, Haochen Chen, and Steven Skiena. Don't walk, skip!: online learning of multi-scale network embeddings. In ASONAM, pages 258-265. ACM, 2017.","title":"Don't walk, skip!: online learning of multi-scale network embeddings","authors":"Bryan Perozzi, Vivek Kulkarni, Haochen Chen, Steven Skiena","year":"2017","venue":"ASONAM","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Qiu et al., 2018] Jiezhong Qiu, Yuxiao Dong, et al. Net- work embedding as matrix factorization: Unifying deep- walk, line, pte, and node2vec. In WSDM, 2018.","title":"Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec","authors":"Qiu","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Sang et al., 2019] Lei Sang, Min Xu, et al. AAANE: Attention-based adversarial autoencoder for multi-scale network embedding. In PAKDD. Springer, 2019.","title":"AAANE: Attention-based adversarial autoencoder for multi-scale network embedding","authors":"Lei Sang, Min Xu","year":"2019","venue":"PAKDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Shen and Chung, 2017] Xiao Shen and Fu-Lai Chung. Deep network embedding with aggregated proximity pre- serving. In ASONAM, pages 40-43. ACM, 2017.","title":"Deep network embedding with aggregated proximity preserving","authors":"Xiao Shen, Fu-Lai Chung","year":"2017","venue":"ASONAM","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Singh et al., 2017] Harvineet Singh, Amitabha Bagchi, and Parag Singla. Learning user representations in online so- cial networks using temporal dynamics of information dif- fusion. arXiv preprint arXiv:1710.07622, 2017.","title":"Learning user representations in online social networks using temporal dynamics of information diffusion","authors":"Singh","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang and Li, 2019] Zhitao Wang and Wenjie Li. Hierar- chical diffusion attention network. In IJCAI, pages 3828- 3834. AAAI Press, 2019.","title":"Hierarchical diffusion attention network","authors":"Wang, Li ;, Zhitao Wang, Wenjie Li","year":"2019","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2019/531"},{"position":24,"raw":"[Wang et al., 2018] Zhitao Wang, Chengyao Chen, and Wen- jie Li. A sequential neural information diffusion model with structure attention. In CIKM. ACM, 2018.","title":"A sequential neural information diffusion model with structure attention","authors":"Wang","year":"2018","venue":"CIKM. ACM","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2019] Zhitao Wang, Chengyao Chen, et al. In- formation diffusion prediction with network regularized role-based user representation learning. TKDD, 2019.","title":"Information diffusion prediction with network regularized role-based user representation learning","authors":"Wang","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Xian et al., 2019] Yikun Xian, Zuohui Fu, S. Muthukrish- nan, et al. Reinforcement knowledge graph reasoning for explainable recommendation. SIGIR, 2019.","title":"Reinforcement knowledge graph reasoning for explainable recommendation","authors":"Xian","year":"2019","venue":"SIGIR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yang and Leskovec, 2011] Jaewon Yang and Jure Leskovec. Patterns of temporal variation in online media. In WSDM, pages 177-186. ACM, 2011.","title":"Patterns of temporal variation in online media","authors":"Jaewon Yang, Jure Leskovec","year":"2011","venue":"WSDM","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang et al., 2017] Cheng Yang, Maosong Sun, Zhiyuan Liu, and Cunchao Tu. Fast network embedding enhancement via high order proximity approximation. In IJCAI, 2017.","title":"Fast network embedding enhancement via high order proximity approximation","authors":"Cheng Yang, Maosong Sun, Zhiyuan Liu, Cunchao Tu","year":"2017","venue":"IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2017/544"},{"position":29,"raw":"[Yang et al., 2018] Cheng Yang, Maosong Sun, Haoran Liu, Shiyi Han, et al. Neural diffusion model for microscopic cascade prediction. CoRR, abs/1812.08933, 2018.","title":"Neural diffusion model for microscopic cascade prediction","authors":"Cheng Yang, Maosong Sun, Haoran Liu, Shiyi Han","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Yang et al., 2019] Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, et al. Multi-scale information diffusion pre- diction with reinforced recurrent networks. In AAAI, 2019.","title":"Multi-scale information diffusion prediction with reinforced recurrent networks","authors":"Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui","year":"2019","venue":"AAAI","doi":null,"url":null,"internal_url":"/proceedings/2019/560"},{"position":31,"raw":"[Zhang et al., 2017] Yuan Zhang, Tianshu Lyu, and Yan Zhang. Hierarchical community-level information diffu- sion modeling in social networks. In SIGIR. ACM, 2017.","title":"Hierarchical community-level information diffusion modeling in social networks","authors":"Yuan Zhang, Tianshu Lyu, Yan Zhang","year":"2017","venue":"SIGIR. ACM","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Zhang et al., 2018] Yuan Zhang, Tianshu Lyu, et al. Co- sine: Community-preserving social network embedding from information diffusion cascades. In AAAI, 2018.","title":"Cosine: Community-preserving social network embedding from information diffusion cascades","authors":"Yuan Zhang, Tianshu Lyu","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}