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Paper List of Information Diffusion Prediction

Contributed by Hao Wang, Haoran Wang and Cheng Yang from School of Computer Science, Beijing University of Posts and Telecommunications.

Survey

  1. A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances. Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang. ACM Computing Surveys (CSUR), vol. 54, no. 2, article 27, 36 pages, Mar 2021 [link, arXiv]
  2. Taxonomy and Evaluation for Microblog Popularity Prediction. Xiaofeng Gao, Zhenhao Cao, Sha Li, Bin Yao, Guihai Chen, Shaojie Tang. TKDD 2019. paper

2021

  1. Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction Xiangyun Xiangyun Tang, Dongliang Liao, Weijie Huang, Liehuang Zhu, Meng Shen, and Jin Xu. AAAI, 2021, pp.582-590. paper
  2. CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction. Fan Zhou, Xovee Xu, Kunpeng Zhang, Siyuan Liu and Goce Trajcevski. TKDE, 14 pages, Nov 2021. paper&code
  3. Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction Fan Zhou, Liu Yu, Xovee Xu, and Goce Trajcevski. SIGIR, Virtual Event, Jul 11-15, 2021, pp. 1875-1879. paper

2020

  1. Joint Learning of User Representation with Diffusion Sequence and Network Structure. Wang, Zhitao, Chengyao Chen, and Wenjie Li. TKDE 2020.paper
  2. HID: Hierarchical Multiscale Representation Learning for Information Diffusion. Zhou Honglu, Shuyuan Xu, and Zouhui Fu. IJCAI 2020.paper
  3. Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction. Aravind Sankar, Xinyang Zhang, Adit Krishnan, Jiawei Han. WSDM 2020.paper
  4. Cascade-LSTM: A Tree-Structured Neural Classifier for Detecting Misinformation Cascades. Francesco Ducci, Mathias Kraus, Stefan Feuerriegel. KDD 2020.paper code
  5. Variational Information Diffusion for Probabilistic Cascades Prediction. Fan Zhou, Xovee Xu, Kunpeng Zhang, Goce Trajcevski, Ting Zhong. INFOCOM, Virtual conference, Jul 6-9, 2020, pp. 1618-1627. [paper]
  6. DyHGCN: A Dynamic Heterogeneous Graph Convolutional Network to Learn Users’ Dynamic Preferences for Information Diffusion Prediction. Chunyuan Yuan, Jiacheng Li, Wei Zhou, Yijun Lu, Xiaodan Zhang, and Songlin Hu. ECMLPKDD 2020. paper
  7. A Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific Impact. Fan Zhou, Xovee Xu, Ce Li, Goce Trajcevski, Ting Zhong and Kunpeng Zhang. arXiv 2020. paper code
  8. Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks. Sameera Horawalavithana, John Skvoretz, Adriana Iamnitchi. arXiv 2020. paper
  9. Contrastive Cascade Graph Learning Xovee Xu, Fan Zhou, Kunpeng Zhang, and Goce Trajcevski. Under review. paper&code
  10. Predicting Information Diffusion Cascades Using Graph Attention Networks Meng Wang, and Kan Li International Conference on Neural Information Processing (ICONIP), 2020, pp. 104-112
  11. Popularity prediction on social platforms with coupled graph neural networks. Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, Xueqi Cheng. WSDM 2020.

2019

  1. Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks. Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu. IJCAI 2019.paper
  2. Neural diffusion model for microscopic cascade study. Cheng Yang, Maosong Sun, Haoran Liu,Shiyi Han, Zhiyuan Liu, and Huanbo Luan. TKDE 2019. paper
  3. Information Cascades Modeling via Deep Multi-Task Learning. Xueqin Chen, Kunpeng Zhang, Fan Zhou, Goce Trajcevski, Ting Zhong, and Fengli Zhang. SIGIR 2019. paper
  4. Understanding Information Diffusion via Heterogeneous Information Network Embeddings. Yuan Su, Xi Zhang, Senzhang Wang, Binxing Fang, Tianle Zhang, Philip S. Yu. DASFAA 2019. paper
  5. NPP: A neural popularity prediction model for social media content. Guandan Chen, Qingchao Kong, Nan Xu, Wenji Mao. Neurocomputing 2019. paper
  6. DeepFork: Supervised Prediction of Information Diffusion in GitHub. Ramya Akula, Niloofar Yousefi, Ivan Garibay. paper
  7. Information Diffusion Prediction via Recurrent Cascades Convolution. Xueqin Chen, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Fengli Zhang. IEEE ICDE 2019. paper
  8. Deep Learning Approach on Information Diffusion in Heterogeneous Networks. Soheila Molaei, Hadi Zare, Hadi Veisi. KBS 2019. paper
  9. Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks. Zhenhua Huang, Zhenyu Wang, Rui Zhang. IEEE Access 2019. paper
  10. Prediction of Information Cascades via Content and Structure Integrated Whole Graph Embedding. Xiaodong Feng, Qiang Zhao, Zhen Liu. BSMDMA 2019. paper
  11. COSINE: Community-Preserving Social Network Embedding From Information Diffusion Cascades. Yuan Zhang, Tianshu Lyu, Yan Zhang. AAAI 2019. paper
  12. A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion. Sylvain Lamprier. ICML 2019. paper
  13. Prediction Model for Non-topological Event Propagation in Social Networks. Zitu Liu, Rui Wang, Yong Liu. ICPCSEE 2019. paper
  14. Information Diffusion Prediction with Network Regularized Role-based User Representation Learning. Zhitao Wang, Chengyao Chen, Wenjie Li. TKDD 2019. paper
  15. Understanding the mechanism of social tie in the propagation process of social network with communication channel. Kai Li, Guangxi Lv, Zhefeng Wang, Qi Liu, Enhong Chen, Lisheng Qiao. Frontiers of Computer Science 2019. paper
  16. Hierarchical Diffusion Attention Network. Zhitao Wang, Wenjie Li. IJCAI 2019. paper
  17. Predicting Future Participants of Information Propagation Trees. Hsing-Huan Chung, Hen-Hsen Huang, Hsin-Hsi Chen. IEEE/WIC/ACM International Conference on Web Intelligence 2019. paper
  18. Dual Sequential Prediction Models Linking Sequential Recommendation and Information Dissemination. Qitian Wu, Yirui Gao, Xiaofeng Gao, Paul Weng, and Guihai Chen. KDD 2019. paper
  19. Statistical Inference of Diffusion Networks. Hao Huang, Qian Yan, Lu Chen, Yunjun Gao, Christian S. Jensen. TKDE 2019. paper
  20. Learning Diffusions without Timestamps. Hao Huang, Qian Yan, Ting Gan, Di Niu, Wei Lu, Yunjun Gao. AAAI 2019. paper
  21. Community structure enhanced cascade prediction. Chaochao Liu, Wenjun Wang, Yueheng Sun. Neurocomputing 2019. paper

2018

  1. DeepDiffuse: Predicting the 'Who' and 'When' in Cascades. Sathappan Muthiah, Sathappan Muthiah, Bijaya Adhikari, B. Aditya Prakash, Naren Ramakrishnan. ICDM 2018. paper
  2. A sequential neural information diffusion model with structure attention. Zhitao Wang, Chengyao Chen, and Wenjie Li. CIKM 2018. paper
  3. Attention network for information diffusion prediction. Zhitao Wang, Chengyao Chen, and Wenjie Li. WWW 2018. paper
  4. Inf2vec:Latent representation model for social influence embedding. Shanshan Feng, Gao Cong, Arijit Khan,Xiucheng Li, Yong Liu, and Yeow Meng Chee. ICDE 2018. paper
  5. Who will share my image? Predicting the content diffusion path in online social networks. W. Hu, K. K. Singh, F. Xiao, J. Han, C.-N. Chuah, and Y. J. Lee. WSDM 2018. paper
  6. Learning sequential features for cascade outbreak prediction. Chengcheng Gou, Huawei Shen, Pan Du, Dayong Wu, Yue Liu, Xueqi Cheng. Knowledge and Information System 2018. paper
  7. Predicting the Popularity of Online Content with Knowledge-enhanced Neural Networks. Hongjian Dou, Wayne Xin Zhao, Yuanpei Zhao, Daxiang Dong, Ji-Rong Wen, Edward Y. Chang. KDD 2018. paper
  8. Predicting Temporal Activation Patterns via Recurrent Neural Networks. Giuseppe Manco, Giuseppe Pirrò, Ettore Ritacco. ISMIS 2018. paper
  9. Weighted estimation of information diffusion probabilities for independent cascade model. Yoosof Mashayekhi, Mohammad Reza Meybodi, Alireza Rezvanian. ICWR 2018. paper
  10. Walk prediction in directed networks. Chuankai An, A.James O’Malley, Daniel N.Rockmore. International Conference on Complex Network and their Applications 2018. paper
  11. Modeling Topical Information Diffusion over Microblog Networks. Kuntal Day, Hemank Lamba, Seema Nagar, Shubham Gupta, Saroj Kaushik. International Conference on Complex Network and their Applications 2018. paper
  12. DeepInf: Social Influence Prediction with Deep Learning. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang. KDD 2018. paper
  13. CAS2VEC: Network-Agnostic Cascade Prediction in Online Social Networks. Zekarias T. Kefato, Nasrullah Sheikh, Leila Bahri, Amira Soliman, Alberto Montresor, Sarunas Girdzijauskas. SNAMS 2018. paper
  14. A Variational Topological Neural Model for Cascade-based Diffusion in Networks. Sylvain Lamprier. arXiv 2018. paper
  15. Joint Modeling of Text and Networks for Cascade Prediction. Cheng Li, Xiaoxiao Guo, Qiaozhu Mei. ICWSM 2018. paper
  16. CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion. Avirup Saha, Bidisha Samanta, Niloy Ganguly. CIKM 2018. paper

2017

  1. DeepCas: An end-to-end predictor of information cascades. C. Li, J. Ma, X. Guo, and Q. Mei. WWW 2017. paper
  2. Topological recurrent neural network for diffusion prediction. Jia Wang, Vincent W Zheng, ZeminLiu, and Kevin Chen-Chuan Chang. ICDM 2017. paper
  3. DeepHawkes: Bridging the gap between prediction and understanding of information cascades. Qi Cao, Huawei Shen, Keting Cen, Wentao Ouyang, and Xueqi Cheng. CIKM 2017. paper
  4. Cascade dynamics modeling with attention-based recurrent neural network. Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. IJCAI 2017. paper
  5. A novel embedding method for information diffusion prediction in social network big data. S. Gao, H. Pang, P. Gallinari, J. Guo, and N. Kato. IEEE Transactions on Industrial Informatics 2017. paper
  6. LMPP: A Large Margin Point Process Combining Reinforcement and Competition for Modeling Hashtag Popularity. Bidisha Samanta, Abir De, Abhijnan Chakraborty, Niloy Ganguly. IJCAI 2017. paper
  7. STRM: A sister tweet reinforcement process for modeling hashtag popularity Bidisha Samanta , Abir De, Niloy Ganguly. IEEE INFOCOM 2017. paper
  8. Hierarchical Community-Level Information Diffusion Modeling in Social Networks. Yuan Zhang, Tianshu Lyu, Yan Zhang. SIGIR 2017. paper

2016

  1. Representation learning for information diffusion through social networks: an embedded cascade model. S. Bourigault, S. Lamprier, P. Gallinari. WSDM 2016. paper
  2. Influence learning for cascade diffusion models: focus on partial orders of infections. Sylvain Lamprier, Simon Bourigault, Patrick Gallinari. Social Network Analysis and Mining 2016. paper

2015

  1. Why it happened: Identifying and modeling the reasons of the happening of social events. Yu Rong, Hong Cheng, Zhiyu Mo. KDD 2015. paper
  2. SEISMIC: A self-exciting point process model for predicting tweet popularity. Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, and J. Leskovec. KDD 2015. paper
  3. Modeling and predicting retweeting dynamics on microblogging platforms. S. Gao, J. Ma, and Z. Chen. WSDM 2015. paper
  4. From micro to macro: Uncovering and predicting information cascading process with behavioral dynamics. L. Yu, P. Cui, F. Wang, C. Song, and S. Yang. ICDM 2015. paper

2014

  1. Can cascades be predicted? J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. WWW 2014. paper
  2. Learning social network embeddings for predicting information diffusion. Simon Bourigault, Cedric Lagnier,Sylvain Lamprier, Ludovic Denoyer, and Patrick Gallinari. WSDM 2014. paper
  3. Predicting successful memes using network and community structure. L. Weng, F. Menczer, and Y.-Y. Ahn. ICWSM 2014. paper
  4. Uncovering the structure and temporal dynamics of information propagation. M. G. Rodriguez, J. Leskovec, D. Balduzzi, and B. Scholkopf. NetSci 2014. paper
  5. Mmrate: inferring multi-aspect diffusion networks with multi-pattern cascades. S. Wang, X. Hu, P. S. Yu, and Z. Li. KDD 2014. paper

Before 2014

  1. Cascading outbreak prediction in networks: a data-driven approach. P. Cui, S. Jin, L. Yu, F. Wang, W. Zhu, and S. Yang. KDD 2013. paper
  2. Structure and dynamics of information pathways in online media. M. Gomez Rodriguez, J. Leskovec, and B. Scholkopf. WSDM 2013. paper
  3. Using early view patterns to predict the popularity of youtube videos. H. Pinto, J. M. Almeida, and M. A. Gonc¸alves. WSDM 2013. paper
  4. What’s in a hashtag?: content basedprediction of the spread of ideas in microblogging communities. O. Tsur, A. Rappoport. WSDM 2012. paper
  5. Inferring networks of diffusion and influence. M. Gomez Rodriguez, J. Leskovec, and A. Krause. KDD 2010. paper
  6. On the convexity of latent social network inference. S. Myers and J. Leskovec. NIPS 2010. paper
  7. Learning continuous-time information diffusion model for social behavioral data analysis. K. Saito, M. Kimura, K. Ohara, and H. Motoda. ACML 2009. paper
  8. Prediction of information diffusion probabilities for independent cascade model. K. Saito, R. Nakano, and M. Kimura. KES 2008. paper
  9. Information diffusion through blogspace. D. Gruhl, R. Guha, D. Liben-Nowell, and A. Tomkins. WWW 2004. paper
  10. Maximizing the spread of influence through a social network. David Kempe, Jon Kleinberg, and Eva ´Tardos. KDD 2003. paper
  11. Talk of the network:A complex systems look at the underlying process of word-ofmouth. J. Goldenberg, B. Libai, and E. Muller. Marketing letters 2001. paper