Modeling Events and Interactions through Temporal Processes - A Survey

A list of Point Processes resources.

Methods • Survey • Datasets • References

Methods

Paper	Date
Uncertainty-Aware Anticipation of Activities [Far19]	2019
A Variational Auto-Encoder Model for Stochastic Point Processes [Meh19]	2019
Recurrent Marked Temporal Point Processes: Embedding Event History to Vector [Du16]	2016
When will you do what? - Anticipating Temporal Occurrences of Activity[Far18]	2018
Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction[Tay20a]	2020
Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Prediction of Business Process Models[Tay20b]	2020
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs[Wei20]	2020
What Averages Do Not Tell - Predicting Real Life Processes with Sequential Deep Learning[Ket22]	2021
A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences [Tay21]	2021
What Happens Next? Event Prediction Using a Compositional Neural Network Model[Gra16]	2016
Attentive Neural Point Processes for Event Forecasting[Gu21]	2021
Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information[Oka19]	2019
Deviation-based Marked Temporal Point process for Marker Prediction [Ch21]	2021
SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity [Zha15]	2015
Uncertainty on Asynchronous Time Event Prediction [Cha19]	2019
A Variational Point Process Model for Social Event Sequences [Pan20]	2020
What is More Likely to Happen Next? Video-and-Language Future Event Prediction[Lei20]	2020
Variational Neural Temporal Point Process [Eo22]	2022
Future Event Prediction: I f and When[Neu19]	2019
Wasserstein generative adversarial networks for modeling marked events	2022
Lecture Notes: Temporal Point Processes and the Conditional Intensity Function	2018
Deep Reinforcement Learning of Marked Temporal Point Processes	2018
Learning Temporal Point Processes via Reinforcement Learning	2018
Wasserstein Learning of Deep Generative Point Process Models	2017
Learning Mixture of Neural Temporal Point Processes for Multi-dimensional Event Sequence Clustering	2022
Deep Recurrent Survival Analysis	2019
Deep Structural Point Process for Learning Temporal Interaction Networks	2021
Tracing temporal communities and event prediction in dynamic social networks	2019
A Model-Free Approach to Infer the Diffusion Network from Event Cascade	2016
Intensity-Free Learning of Temporal Point Processes	2019
Self-Attentive Hawkes Process	2020
Neural Spatio-Temporal Point Processes	2021
Time2Vec: Learning a Vector Representation of Time	2019
Transformer Hawkes Process	2020
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process	2017
Modeling the Intensity Function of Point Process Via Recurrent Neural Networks	2017
Neural Survival Recommender[Jin17]	2017
Learning Time Series Associated Event Sequences With Recurrent Point Process Networks	2019
DeepDiffuse: Predicting the 'Who' and 'When' in Cascades	2018
Inf-VAE: A Variational Autoencoder Framework to Integrate Homophily and Influence in Diffusion Prediction	2020
Exploiting Marked Temporal Point Processes for Predicting Activities of Daily Living	2020
Modeling Event Propagation via Graph Biased Temporal Point Process	2020
Learning Neural Point Processes with Latent Graphs	2021
Information Cascading in Social Networks	2021
Learning Conditional Generative Models for Temporal Point Processes	2018
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution	2016
Topological Recurrent Neural Network for Diffusion Prediction	2017
Cascade Dynamics Modeling with Attention-based Recurrent Neural Network	2017
Time-Dependent Representation for Neural Event Sequence Prediction	2018
Modeling Sequential Online Interactive Behaviors with Temporal Point Process	2018
Marked Temporal Dynamics Modeling Based on Recurrent Neural Network	2017
Time Perception Machine: Temporal Point Processes for the When, Where and What of Activity Prediction	2018
Transformer-Based Deep Survival Analysis	2021
Imitation Learning of Neural Spatio-Temporal Point Processes	2021
Calibration and Uncertainty in Neural Time-to-Event Modeling	2020
Temporal Logic Point Processes	2020
Recurrent Point Processes for Dynamic Review Models	2020
How Can Our Tweets Go Viral? Point-Process Modelling of Brand Content	2022
Modeling Marked Temporal Point Process Using Multi-relation Structure RNN	2019
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs	2017
A gan-based framework for modeling hashtag popularity dynamics using assistive information	2020
Adversarial Time-to-Event Modeling	2018
Forecasting Future Action Sequences with Neural Memory Networks	2019
Semi-supervised Learning for Marked Temporal Point Processes	2021
INITIATOR: Noise-contrastive Estimation for Marked Temporal Point Process	2018
Modeling Continuous Time Sequences with Intermittent Observations using Marked Temporal Point Processes	2022
Interpretable Deep Generative Spatio-Temporal Point Processes	2020
HyperHawkes: Hypernetwork based Neural Temporal Point Process	2022
Point Process Flows	2019
ProActive: Self-Attentive Temporal Point Process Flows for Activity Sequences	2022
Time is of the Essence: A Joint Hierarchical RNN and Point Process Model for Time and Item Predictions	2019
Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks	2019
Social lstm: Human trajectory prediction in crowded spaces	2016
Temporally-Consistent Survival Analysis	2022
Variational Policy for Guiding Point Processes	2017
Cheshire: An Online Algorithm for Activity Maximization in Social Networks	2017
Learning and Forecasting Opinion Dynamics in Social Networks	2017
A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering	2017
Modeling the Dynamics of Learning Activity on the Web	2017
Uncovering Causality from Multivariate Hawkes Integrated Cumulants	2018
Decoupled Learning for Factorial Marked Temporal Point Processes	2018
Explainable Hyperbolic Temporal Point Process for User-Item Interaction Sequence Generation	2022
Counterfactual Phenotyping with Censored Time-to-Events	2022
Continual Learning for Time-to-Event Modeling	2022

Survey

Paper	Date
Neural Temporal Point Process: A Review[Shc21]	2021
Recent Advance in Temporal Point Process: from Machine Learning Perspective[Yan19]	2019
Machine Learning for Survival Analysis: A Survey[Wan19]	2019
Influence Maximization on Social Graphs: A Survey[Li18]	2018
Deep Learning for Social Network Information Cascade Analysis: a survey[Gao20]	2020
Spatio-temporal point process statistics: A review[Gon16]	2016
Event Prediction in the Big Data Era: A Systematic Survey[Zhao22]	2022
Real-world diffusion dynamics based on point process approaches: a review[Kim20]	2020

Datasets

A list of the relevant datasets is available here.

References

• [Cha19] B Charpentier, M. Bilos, S, Gunnemann. Uncertainty on Asynchronous Time Event Prediction. Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019

• [Ch21] A. V. S. Chauhan, S. Reddy, M. Singh, K. Singh, T. Bhowmik. Deviation-based Marked Temporal Point process for Marker Prediction. (2021).

• [Du16] N. Du, H. Dai, R. Trivedi, U. Upadhyay, M. Gomez-Rodriguez, L. Song. Recurrent Marked Temporal Point Processes: Embedding event history to vector. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (2016)

• [Eo22] D. Eom, S. Lee, J. Choi. Variational Neural Temporal Point Process. (2022).

• [Far19] Y. A. Farha, J. Gall. Uncertainty-Aware Anticipation of Activities. (2019).

• [Far18] Y. A. Farha, A. Richard, J. Gall. When Will You Do What? - Anticipating Temporal Occurrences of Activities. IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2018)

• [Gra16] M. Granroth-Wilding, S. Clark. What Happens Next? Event Prediction Using a Compositional Neural Network Model. Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence (2016)

• [Gu21] Y. Gu. Attentive Neural Point Processes for Event Forecasting. Thirty-Fifth {AAAI} Conference on Artificial Intelligence, {AAAI} (2021)

• [Ket22] I. Ketykó, F. Mannhardt, M. Hassani, B. F. van Dongen. What averages do not tell: predicting real life processes with sequential deep learning. SAC '22: The 37th ACM/SIGAPP Symposium on Applied Computing (2022).

• [Meh19] N. Mehrasa, A. A. Jyothi, T. Durand, J. He, L. Sigal, G. Mori. A Variational Auto-Encoder Model for Stochastic Point Processes. (2019).

• [Oka19] M. Okawa, T. Iwata, T. Kurashima, Y. Tanaka, H. Toda, N. Ueda. Deep Mixture Point Processes: Spatio-temporal Event Prediction with Rich Contextual Information. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2019)

• [Tay21] F. Taymouri, M. La Rosa, S. M. Erfani. A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences. Proceedings of the 2021 SIAM International Conference on Data Mining (2021).

• [Tay20a] F. Taymouri, M. La Rosa, S. M. Erfani, Z. D. Bozorgi, I. Verenich. Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction. Business Process Management - 18th International Conference (2020)

• [Tay20b] F. Taymouri, M. La Rosa. Encoder-Decoder Generative Adversarial Nets for Suffix Generation and Remaining Time Predication of Business Process Models. (2020)

• [Wei20] S. Weinzierl, S. Zilker, J. Brunk, K. Revoredo, A. Nguyen, M. Matzner, J. Becker, B. M. Eskofier. An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. (2020)

• [Zha15] Q. Zhao, M. A. Erdogdu, H. Y. He, A. Rajaraman, J. Leskovec. SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2015)

• [Pan20] Z. Pan, Z. Huang, D. Lian, E. Chen. A Variational Point Process Model for Social Event Sequences. The Thirty-Fourth {AAAI} Conference on Artificial Intelligence, {AAAI} 2020 (2020)

• [Lei20] J. Lei, L. Yu, T. L. Berg, M. Bansal. What is More Likely to Happen Next? Video-and-Language Future Event Prediction. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, {EMNLP} 2020 (2020)

• [Eom22] D. Eom, S. Lee, J. Choi. Variational Neural Temporal Point Process (2022)

• [Neu19] L. Neumann, A. Zisserman, A. Vedaldi. Future Event Prediction: If and When. {IEEE} Conference on Computer Vision and Pattern Recognition Workshops, {CVPR} Workshops 2019 (2019)

• [Diz22] S.H.S. Dizaji, S. Pashazadeh, J.M. Niya. Wasserstein generative adversarial networks for modeling marked events. The Journal of Supercomputing. (2022)

• [Shc21] O. Shchur, A. C. Turkmen, T. Januschowski, S. Gunnemann. Neural Temporal Point Processes: A Review. Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, {IJCAI} 2021 (2021)

• [Yan19] J. Yan. Recent Advance in Temporal Point Process : from Machine Learning Perspective (2019)

• [Ras18] J. G. Rasmussen. Lecture Notes: Temporal Point Processes and the Conditional Intensity Function (2018)

• [Upa18] U. Upadhyay, A. De, M. G. Rodriguez. Deep Reinforcement Learning of Marked Temporal Point Processes. Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018 (2018)

• [Li18] S. Li, Shuai Xiao, Shixiang Zhu, Nan Du, Yao Xie, Le Song. Learning Temporal Point Processes via Reinforcement Learning. Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018. (2018)

• [Xia17] Shuai Xiao and Mehrdad Farajtabar and Xiaojing Ye and Junchi Yan and Xiaokang Yang and Le Song and Hongyuan Zha. Wasserstein Learning of Deep Generative Point Process Models. Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. (2017)

• [Zha22] Yunhao Zhang and Junchi Yan and Xiaolu Zhang and Jun Zhou and Xiaokang Yang. Learning Mixture of Neural Temporal Point Processes for Multi-dimensional Event Sequence Clustering. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI} 2022 (2022)

• [Wan19] Ping Wang and Yan Li and Chandan K. Reddy. Machine Learning for Survival Analysis: {A} Survey. {ACM} Comput. Surv. (2019)

• [Ren19] Kan Ren and Jiarui Qin and Lei Zheng and Zhengyu Yang and Weinan Zhang and Lin Qiu and Yong Yu. Deep Recurrent Survival Analysis. The Thirty-Third {AAAI} Conference on Artificial Intelligence, {AAAI} 2019 (2019)

• [Cao21] Jiangxia Cao and Xixun Lin and Xin Cong and Shu Guo and Hengzhu Tang and Tingwen Liu and Bin Wang. Deep Structural Point Process for Learning Temporal Interaction Networks. Machine Learning and Knowledge Discovery in Databases. Research Track - European Conference, {ECML} {PKDD} 2021 (2021)

• [Kha19] Taleb Khafaei and Alireza Tavakoli Targhi and Mehdi Hosseinzadeh and Ali Rezaee. Tracing temporal communities and event prediction in dynamic social networks. Soc. Netw. Anal. Min. (2019)

• [Ron16] Y. Rong and Qiankun Zhu and Hong Cheng. A Model-Free Approach to Infer the Diffusion Network from Event Cascade. Proceedings of the 25th {ACM} International Conference on Information and Knowledge Management, {CIKM} 2016 (2016)

• [Li18] Yuchen Li and Ju Fan and Yanhao Wang and Kian{-}Lee Tan. Influence Maximization on Social Graphs: {A} Survey. {IEEE} Trans. Knowl. Data Eng. (2018)

• [Shc20] Oleksandr Shchur and Marin Bilos and Stephan G{"{u}}nnemann. Intensity-Free Learning of Temporal Point Processes. 8th International Conference on Learning Representations, {ICLR} 2020 (2020)

• [Zha20] Qiang Zhang and Aldo Lipani and {"{O}}mer Kirnap and Emine Yilmaz. Self-Attentive Hawkes Process. Proceedings of the 37th International Conference on Machine Learning, {ICML} 2020 (2020)

• [Che21] Ricky T. Q. Chen and Brandon Amos and Maximilian Nickel. Neural Spatio-Temporal Point Processes. 9th International Conference on Learning Representations, {ICLR} 2021 (2021)

• [Kaz19] Seyed Mehran Kazemi and Rishab Goel and Sepehr Eghbali and Janahan Ramanan and Jaspreet Sahota and Sanjay Thakur and Stella Wu and Cathal Smyth and Pascal Poupart and Marcus A. Brubake. Time2Vec: Learning a Vector Representation of Time. (2019)

• [Zuo20] Simiao Zuo and Haoming Jiang and Zichong Li and Tuo Zhao and Hongyuan Zha. Transformer Hawkes Process. Proceedings of the 37th International Conference on Machine Learning, {ICML} 2020 (2020)

• [Mei17] Hongyuan Mei and Jason Eisner. The Neural Hawkes Process: {A} Neurally Self-Modulating Multivariate Point Process. Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017. (2017)

• [Xia17] Shuai Xiao and Junchi Yan and Xiaokang Yang and Hongyuan Zha and Stephen M. Chu. Modeling the Intensity Function of Point Process Via Recurrent Neural Networks. Proceedings of the Thirty-First {AAAI} Conference on Artificial Intelligence. (2017)

• [Jin17] How Jing and Alexander J. Smola. Neural Survival Recommender. Proceedings of the Tenth {ACM} International Conference on Web Search and Data Mining, {WSDM} 2017. (2017)

• [Xia19] Shuai Xiao and Junchi Yan and Mehrdad Farajtabar and Le Song and Xiaokang Yang and Hongyuan Zha. Learning Time Series Associated Event Sequences With Recurrent Point Process Networks. {IEEE} Trans. Neural Networks Learn. Syst. (2019)

• [Gao20] Liqun Gao and Bin Zhou and Yan Jia and Hongkui Tu and Ye Wang and Chenguang Chen and Haiyang Wang and Hongwu Zhuang. Deep Learning for Social Network Information Cascade Analysis: a survey. 5th {IEEE} International Conference on Data Science in Cyberspace, {DSC} (2020)

• [Gon16] Jonatan A. González and Francisco J. Rodríguez-Cortés and Ottmar Cronie and Jorge Mateu. Spatio-temporal point process statistics: A review. Spatial Statistics. (2016)

• [Zhao22] Liang Zhao. Event Prediction in the Big Data Era: {A} Systematic Survey. {ACM} Comput. Surv. (2022)

• [Kim20] Minkyoung Kim and Dean Paini and Raja Jurdak. Real-world diffusion dynamics based on point process approaches: a review. Artif. Intell. Rev. (2020)