Paper List of Deep Learning-based Information Diffusion Modeling

Contributed by Xueqin Chen from University of Electronic Science and Technology of China (UESTC) and Leiden University. Thanks for Xovee's suggestion for improvements!

Information cascades modeling is accomplished via specific prediction tasks, which are categorized into two levels: Micro-level and Macro-level. (1) At micro-level, local patterns of social influence are studied -- e.g., inferring the action status of a user. The approaches predict the likelihood of a user propagating a particular piece of information, or forecast when the next propagation might occur given a certain information cascade. (2) At macro-level, typical studies include cascade size prediction and outbreak prediction (above a certain threshold), both cascade size prediction and outbreak prediction are aiming to estimate the future size (popularity) of the diffusion cascade.

Highly recommended the following survey papers:

1. Graph representation learning for popularity prediction problem: a survey. Tiantian Chen, Jianxiong Guo, Weili Wu. Discrete Mathematics, Algorithms and Applications 2022. paper

2. A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances. Fan Zhou, Xovee Xu, Goce Trajcevski, Kunpeng Zhang. CSUR 2021. paper

3. Survey on Deep Learning Based Popularity Prediction (in Chinese). Qi Cao, Huawei Shen，Jinhua Gao，Xueqi Cheng. (Macro-level) Journal of Chinese Information Processing 2021. paper

Micro-level

1. DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction. Ruijie Wang, Zijie Huang, Shengzhong Liu, Huajie Shao, Dongxin Liu, Jinyang Li, Tianshi Wang, Dachun Sun, Shuochao Yao, Tarek Abdelzaher. SIGIR 2021. paper
2. Information Diffusion Prediction via Dynamic Graph Neural Networks. Zongmai Cao; Kai Han; Jianfu Zhu. CSCWD 2021. paper
3. Neural Information Diffusion Prediction with Topic-Aware Attention Network. Hao Wang, Cheng Yang, Chuan Shi. CIKM 2021. paper code
4. Joint Learning of User Representation with Diffusion Sequence and Network Structure. Zhitao Wang, Chengyao Chen, and Wenjie Li. TKDE 2020. paper
5. HID: Hierarchical Multiscale Representation Learning for Information Diffusion. Zhou Honglu, Shuyuan Xu, and Zouhui Fu. IJCAI 2020. paper code
6. Cascade-LSTM: Predicting Information Cascades using Deep Neural Networks. Sameera Horawalavithana, John Skvoretz, Adriana Iamnitchi. arXiv 2020. paper
7. 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 code
8. 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
9. Neural diffusion model for microscopic cascade study. Cheng Yang, Maosong Sun, Haoran Liu,Shiyi Han, Zhiyuan Liu, and Huanbo Luan. TKDE 2019. paper
10. Understanding Information Diffusion via Heterogeneous Information Network Embeddings. Yuan Su, Xi Zhang, Senzhang Wang, Binxing Fang, Tianle Zhang, Philip S. Yu. DASFAA 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 code
13. Information Diffusion Prediction with Network Regularized Role-based User Representation Learning. Zhitao Wang, Chengyao Chen, Wenjie Li. TKDD 2019. paper
14. Hierarchical Diffusion Attention Network. Zhitao Wang, Wenjie Li. IJCAI 2019. paper code
15. Predicting Future Participants of Information Propagation Trees. Hsing-Huan Chung, Hen-Hsen Huang, Hsin-Hsi Chen. WI 2019. paper
16. Community structure enhanced cascade prediction. Chaochao Liu, Wenjun Wang, Yueheng Sun. Neurocomputing 2019. paper
17. DeepDiffuse: Predicting the 'Who' and 'When' in Cascades. Sathappan Muthiah, Sathappan Muthiah, Bijaya Adhikari, B. Aditya Prakash, Naren Ramakrishnan. ICDM 2018. paper code
18. A sequential neural information diffusion model with structure attention. Zhitao Wang, Chengyao Chen, and Wenjie Li. CIKM 2018. paper code
19. Attention network for information diffusion prediction. Zhitao Wang, Chengyao Chen, and Wenjie Li. WWW 2018. paper
20. 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
21. 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
22. Predicting Temporal Activation Patterns via Recurrent Neural Networks. Giuseppe Manco, Giuseppe Pirrò, Ettore Ritacco. ISMIS 2018. paper
23. DeepInf: Social Influence Prediction with Deep Learning. Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang. KDD 2018. paper code
24. A Variational Topological Neural Model for Cascade-based Diffusion in Networks. Sylvain Lamprier. arXiv 2018. paper
25. Topological recurrent neural network for diffusion prediction. Jia Wang, Vincent W Zheng, ZeminLiu, and Kevin Chen-Chuan Chang. ICDM 2017. paper code
26. Cascade dynamics modeling with attention-based recurrent neural network. Yongqing Wang, Huawei Shen, Shenghua Liu, Jinhua Gao, and Xueqi Cheng. IJCAI 2017. paper code

Macro-level

1. Information Diffusion Prediction via Exploiting Cascade Relationship Diversity. Xigang Sun, Jingya Zhou, Zhen Wu, Jie Wang. CSCWD 2023. paper

2. Explicit time embedding based cascade attention network for information popularity prediction. Xigang Sun, Jingya Zhou, Ling Liu, Wenqi Wei. Information Processing & Management 2023. paper

3. Wb-MSF: A Large-scale Multi-source Information Diffusion Dataset for Social Information Diffusion Prediction. Zhen Wu, Jingya Zhou, Jie Wang, Xigang Sun. CBD 2022 paper

4. Deep Popularity Prediction in Multi-Source Cascade with HERI-GCN. Wu Zhen, Jingya Zhou, Ling Liu, Chaozhuo Li, Fei Gu. ICDE 2022. paper code

5. AECasN: An information cascade predictor by learning the structural representation of the whole cascade network with autoencoder. Xiaodong Feng, Qihang Zhao, Yunkai Li. Expert Systems With Applications 2021. paper

6. Pre-training of Temporal Convolutional Neural Networks for Popularity Prediction. Qi Cao, Huawei Shen, Yuanhao Liu, Jinhua Gao, Xueqi Cheng. arXiv 2021. paper

7. Decoupling Representation and Regressor for Long-Tailed Information Cascade Prediction. Fan Zhou, Liu Yu, Xovee Xu, Goce Trajcevski. SIGIR 2021. paper

8. CasSeqGCN: Combining Network Structure and Temporal Sequence to Predict Information Cascades. Yansong Wang, Xiaomeng Wang, Radosław Michalski, Yijun Ran, Tao Jia. arXiv 2021. paper code

9. CasGCN: Predicting future cascade growth based on information diffusion graph. Zhixuan Xu, Minghui Qian, Xiaowei Huang, Jie Meng. arXiv 2021. paper

10. CCGL: Contrastive Cascade Graph Learning Xovee Xu, Fan Zhou, Kunpeng Zhang, and Siyuan Liu. arXiv 2021. paper code

11. Prediction of information cascades via content and structure proximity preserved graph level embedding. Xiaodong Feng, Qihang Zhao, Zhen Liu. Information Sciences 2021. paper

12. Fully Exploiting Cascade Graphs for Real-time Forwarding Prediction. Xiangyun Tang, Dongliang Liao, Weijie Huang, Jin Xu, Liehuang Zhu, Meng Shen. AAAI 2021. paper code

13. A Feature Generalization Framework for Social Media Popularity Prediction. Kai Wang, Penghui Wang, Xin Chen, Qiushi Huang, Zhendong Mao, Yongdong Zhang. MM 2020. paper

14. Variational Information Diffusion for Probabilistic Cascades Prediction. Fan Zhou, Xovee Xu, Kunpeng Zhang, Goce Trajcevski, Ting Zhong. INFOCOM 2020. paper

15. 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

16. CasFlow: Exploring Hierarchical Structures and Propagation Uncertainty for Cascade Prediction. Fan Zhou, Xovee Xu, Kunpeng Zhang, Siyuan Liu and Goce Trajcevski. arXiv 2020. paper code

17. Continual Information Cascade Learning. Fan Zhou, Xin Jing, Xovee Xu, Ting Zhong, Goce Trajcevski, Jin Wu. GLOBECOM 2020. paper

18. Coupled Graph Neural Networks for Predicting the Popularity of Online Content. Qi Cao, Huawei Shen, Jinhua Gao, Bingzheng Wei, Xueqi Cheng. WSDM 2020. paper code

19. Learning Bi-directional Social Influence in Information Cascades using Graph Sequence Attention Networks. Zhenhua Huang, Zhenyu Wang, Rui Zhang, Yangyang Zhao, Fadong Zheng. WWW 2020. paper

20. NPP: A neural popularity prediction model for social media content. Guandan Chen, Qingchao Kong, Nan Xu, Wenji Mao. Neurocomputing 2019. paper

21. Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks. Zhenhua Huang, Zhenyu Wang, Rui Zhang. IEEE Access 2019. paper code

22. Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features. Dongliang Liao, Jin Xu, Gongfu Li, Weijie Huang, Weiqing Liu, Jing Li. AAAI 2019. paper

23. Information Diffusion Prediction via Recurrent Cascades Convolution. Xueqin Chen, Fan Zhou, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Fengli Zhang. IEEE ICDE 2019. paper code

24. Deep Learning Approach on Information Diffusion in Heterogeneous Networks. Soheila Molaei, Hadi Zare, Hadi Veisi. KBS 2019. paper

25. Prediction of Information Cascades via Content and Structure Integrated Whole Graph Embedding. Xiaodong Feng, Qiang Zhao, Zhen Liu. BSMDMA 2019. paper

26. Prediction Model for Non-topological Event Propagation in Social Networks. Zitu Liu, Rui Wang, Yong Liu. ICPCSEE 2019. paper

27. 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

28. User-guided hierarchical attention network for multi-modal social image popularity prediction Wei Zhang, Wen Wang, Jun Wang, Hongyuan Zha. WWW 2018. paper code

29. Factorization Meets Memory Network: Learning to Predict Activity Popularity. Wen Wang, Wei Zhang, Jun Wang. DASFAA 2018. paper code

30. 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

31. 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 code

32. Joint Modeling of Text and Networks for Cascade Prediction. Cheng Li, Xiaoxiao Guo, Qiaozhu Mei. ICWSM 2018. paper

33. Sequential prediction of social media popularity with deep temporal context networks. Bo Wu, Wen-Huang Cheng, Yongdong Zhang, Qiushi Huang, Jintao Li, Tao Mei. IJCAI 2017. paper code

34. 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 code

35. DeepCas: An end-to-end predictor of information cascades. C. Li, J. Ma, X. Guo, and Q. Mei. WWW 2017. paper code

Micro + Macro

1. Full-Scale Information Diffusion Prediction With Reinforced Recurrent Networks. Cheng Yang, Hao Wang, Jian Tang, Chuan Shi, Maosong Sun, Ganqu Cui, Zhiyuan Liu. TNNLS 2021. paper code
2. Multi-scale Information Diffusion Prediction with Reinforced Recurrent Networks. Cheng Yang, Jian Tang, Maosong Sun, Ganqu Cui, Zhiyuan Liu. IJCAI 2019. paper code
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. CRPP: Competing Recurrent Point Process for Modeling Visibility Dynamics in Information Diffusion. Avirup Saha, Bidisha Samanta, Niloy Ganguly. CIKM 2018. paper code