DST: : Continual event prediction by decomposing and synergizing the task commonality and specificity
Authors: 
Yuxin Zhang, Songlin Zhai, Yongrui Chen, Shenyu Zhang, Sheng Bi, Yuan Meng, Guilin QiAuthors Info & Claims
References
[1]
Aljundi R., Babiloni F., Elhoseiny M., Rohrbach M., Tuytelaars T., Memory aware synapses: Learning what (not) to forget, in: Ferrari V., Hebert M., Sminchisescu C., Weiss Y. (Eds.), Computer vision - ECCV 2018 - 15th European conference, munich, Germany, September 8-14, 2018, proceedings, part III, in: Lecture notes in computer science, vol. 11207, Springer, 2018, pp. 144–161,.
[2]
Aljundi R., Lin M., Goujaud B., Bengio Y., Gradient based sample selection for online continual learning, in: Wallach H.M., Larochelle H., Beygelzimer A., d’Alché-Buc F., Fox E.B., Garnett R. (Eds.), Advances in neural information processing systems 32: Annual conference on neural information processing systems 2019, 2019, pp. 11816–11825. URL: https://proceedings.neurips.cc/paper/2019/hash/e562cd9c0768d5464b64cf61da7fc6bb-Abstract.html.
[3]
Amalapuram S.K., Channappayya S.S., Tamma B.R., Augmented memory replay-based continual learning approaches for network intrusion detection, in: Oh A., Naumann T., Globerson A., Saenko K., Hardt M., Levine S. (Eds.), Advances in neural information processing systems 36: Annual conference on neural information processing systems 2023, 2023, p. 1. URL: http://papers.nips.cc/paper_files/paper/2023/hash/3755a02b1035fbadd5f93a022170e46f-Abstract-Conference.html.
[4]
Cai J., Wang X., Guan C., Tang Y., Xu J., Zhong B., et al., Multimodal continual graph learning with neural architecture search, in: Laforest F., Troncy R., Simperl E., Agarwal D., Gionis A., Herman I., Médini L. (Eds.), The ACM web conference 2022, virtual event, lyon, France, April 25 - 29, 2022, ACM, 2022, pp. 1292–1300,.
[5]
Chen Y., Guo X., Wu T., Qi G., Li Y., Dong Y., Learn from yesterday: A semi-supervised continual learning method for supervision-limited text-to-SQL task streams, in: Williams B., Chen Y., Neville J. (Eds.), Thirty-seventh AAAI conference on artificial intelligence, AAAI 2023, thirty-fifth conference on innovative applications of artificial intelligence, IAAI 2023, thirteenth symposium on educational advances in artificial intelligence, EAAI 2023, washington, DC, USA, February 7-14, 2023, AAAI Press, 2023, pp. 12682–12690,.
[6]
Chen Y., Zhang S., Qi G., Guo X., Parameterizing context: Unleashing the power of parameter-efficient fine-tuning and in-context tuning for continual table semantic parsing, 2023,. CoRR abs/2310.04801, arXiv:2310.04801.
[7]
Chrysakis A., Moens M., Online continual learning from imbalanced data, in: Proceedings of the 37th international conference on machine learning, ICML 2020, 13-18 July 2020, virtual event, in: Proceedings of machine learning research, vol. 119, PMLR, 2020, pp. 1952–1961. URL: http://proceedings.mlr.press/v119/chrysakis20a.html.
[8]
de Masson d’Autume C., Ruder S., Kong L., Yogatama D., Episodic memory in lifelong language learning, in: Wallach H.M., Larochelle H., Beygelzimer A., d’Alché-Buc F., Fox E.B., Garnett R. (Eds.), Advances in neural information processing systems 32: Annual conference on neural information processing systems 2019, 2019, pp. 13122–13131. URL: https://proceedings.neurips.cc/paper/2019/hash/f8d2e80c1458ea2501f98a2cafadb397-Abstract.html.
[9]
Fan W., Jin R., Lu P., Tian C., Xu R., Towards event prediction in temporal graphs, Proceedings of the VLDB Endowment 15 (9) (2022) 1861–1874,. URL: https://www.vldb.org/pvldb/vol15/p1861-tian.pdf.
[10]
Granroth-Wilding M., Clark S., What happens next? Event prediction using a compositional neural network model, in: Schuurmans D., Wellman M.P. (Eds.), Proceedings of the thirtieth AAAI conference on artificial intelligence, February 12-17, 2016, phoenix, arizona, USA, AAAI Press, 2016, pp. 2727–2733,.
[11]
Han S., Huang H., Huang X., Li Y., Yu R., Zhang J., Core patent forecasting based on graph neural networks with an application in stock markets, Technology Analysis & Strategic Management 36 (8) (2024) 1680–1694,.
[12]
Huang D., Lei F., Temporal group-aware graph diffusion networks for dynamic link prediction, Information Processing and Management 60 (3) (2023),.
[13]
Kirkpatrick J., Pascanu R., Rabinowitz N.C., Veness J., Desjardins G., Rusu A.A., et al., Overcoming catastrophic forgetting in neural networks, 2016, CoRR abs/1612.00796, arXiv:1612.00796, URL: http://arxiv.org/abs/1612.00796.
[14]
Lester B., Al-Rfou R., Constant N., The power of scale for parameter-efficient prompt tuning, in: Moens M., Huang X., Specia L., Yih S.W. (Eds.), Proceedings of the 2021 conference on empirical methods in natural language processing, EMNLP 2021, virtual event / punta cana, dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, 2021, pp. 3045–3059,.
[15]
Letham B., Rudin C., Madigan D., Sequential event prediction, Machine Learning 93 (2–3) (2013) 357–380,.
[16]
Letham B., Rudin C., McCormick T.H., Madigan D., Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model, 2015, CoRR abs/1511.01644, arXiv:1511.01644, URL: http://arxiv.org/abs/1511.01644.
[17]
Li M., Li S., Wang Z., Huang L., Cho K., Ji H., et al., The future is not one-dimensional: Complex event schema induction by graph modeling for event prediction, in: Moens M., Huang X., Specia L., Yih S.W. (Eds.), Proceedings of the 2021 conference on empirical methods in natural language processing, EMNLP 2021, virtual event / punta cana, dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, 2021, pp. 5203–5215,.
[18]
Li Z., Qu L., Haffari G., Total recall: A customized continual learning method for neural semantic parsers, in: Moens M., Huang X., Specia L., Yih S.W. (Eds.), Proceedings of the 2021 conference on empirical methods in natural language processing, EMNLP 2021, virtual event / punta cana, dominican Republic, 7-11 November, 2021, Association for Computational Linguistics, 2021, pp. 3816–3831,.
[19]
Li Y., Yang X., Wang H., Wang X., Li T., Learning to prompt knowledge transfer for open-world continual learning, in: Wooldridge M.J., Dy J.G., Natarajan S. (Eds.), Thirty-eighth AAAI conference on artificial intelligence, AAAI 2024, thirty-sixth conference on innovative applications of artificial intelligence, IAAI 2024, fourteenth symposium on educational advances in artificial intelligence, EAAI 2014, February 20-27, 2024, vancouver, Canada, AAAI Press, 2024, pp. 13700–13708,.
[20]
Li Q., You T., Chen J., Zhang Y., Du C., BioDynGrap: Biomedical event prediction via interpretable learning framework for heterogeneous dynamic graphs, Expert Systems with Applications 244 (2024),.
[21]
Liu S., Wang X., Macdonald C., Ounis I., A social-aware Gaussian pre-trained model for effective cold-start recommendation, Information Processing and Management 61 (2) (2024),.
[22]
Liu H., Yang Y., Wang X., Overcoming catastrophic forgetting in graph neural networks, in: Thirty-fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, the eleventh symposium on educational advances in artificial intelligence, EAAI 2021, virtual event, February 2-9, 2021, AAAI Press, 2021, pp. 8653–8661,.
[23]
Liu Z., Yu X., Fang Y., Zhang X., GraphPrompt: Unifying pre-training and downstream tasks for graph neural networks, in: Ding Y., Tang J., Sequeda J.F., Aroyo L., Castillo C., Houben G. (Eds.), Proceedings of the ACM web conference 2023, WWW 2023, austin, TX, USA, 30 April 2023 - 4 May 2023, ACM, 2023, pp. 417–428,.
[24]
Lopez-Paz D., Ranzato M., Gradient episodic memory for continual learning, in: Guyon I., von Luxburg U., Bengio S., Wallach H.M., Fergus R., Vishwanathan S.V.N., Garnett R. (Eds.), Advances in neural information processing systems 30: Annual conference on neural information processing systems 2017, December 4-9, 2017, long beach, CA, USA, 2017, pp. 6467–6476. URL: https://proceedings.neurips.cc/paper/2017/hash/f87522788a2be2d171666752f97ddebb-Abstract.html.
[25]
Lu C., Han T., Ning Y., Context-aware health event prediction via transition functions on dynamic disease graphs, in: Thirty-sixth AAAI conference on artificial intelligence, AAAI 2022, thirty-fourth conference on innovative applications of artificial intelligence, IAAI 2022, the twelveth symposium on educational advances in artificial intelligence, EAAI 2022 virtual event, February 22 - March 1, 2022, AAAI Press, 2022, pp. 4567–4574,.
[26]
Lv S., Qian W., Huang L., Han J., Hu S., SAM-net: Integrating event-level and chain-level attentions to predict what happens next, in: The thirty-third AAAI conference on artificial intelligence, AAAI 2019, the thirty-first innovative applications of artificial intelligence conference, IAAI 2019, the ninth AAAI symposium on educational advances in artificial intelligence, EAAI 2019, honolulu, hawaii, USA, January 27 - February 1, 2019, AAAI Press, 2019, pp. 6802–6809,.
[27]
McDonnell M.D., Gong D., Parvaneh A., Abbasnejad E., van den Hengel A., RanPAC: Random projections and pre-trained models for continual learning, in: Oh A., Naumann T., Globerson A., Saenko K., Hardt M., Levine S. (Eds.), Advances in neural information processing systems 36: Annual conference on neural information processing systems 2023, 2023, p. 1. URL: http://papers.nips.cc/paper_files/paper/2023/hash/2793dc35e14003dd367684d93d236847-Abstract-Conference.html.
[28]
Mi F., Chen L., Zhao M., Huang M., Faltings B., Continual learning for natural language generation in task-oriented dialog systems, in: Cohn T., He Y., Liu Y. (Eds.), Findings of the association for computational linguistics: EMNLP 2020, online event, 16-20 November 2020, in: Findings of ACL, Association for Computational Linguistics, 2020, pp. 3461–3474,.
[29]
Radinsky K., Davidovich S., Markovitch S., Learning causality for news events prediction, in: Mille A., Gandon F., Misselis J., Rabinovich M., Staab S. (Eds.), Proceedings of the 21st world wide web conference 2012, ACM, 2012, pp. 909–918,.
[30]
Rama-Maneiro E., Vidal J.C., Lama M., Deep learning for predictive business process monitoring: Review and benchmark, IEEE Transactions on Services Computing 16 (1) (2023) 739–756,.
[31]
Sano H.H., Berton L., A temporal event graph approach and robustness analysis for air transport network, IEEE Transactions on Network Science and Engineering 8 (4) (2021) 3453–3464,.
[32]
Shervashidze N., Schweitzer P., van Leeuwen E.J., Mehlhorn K., Borgwardt K.M., Weisfeiler-lehman graph kernels, Journal of Machine Learning Research 12 (2011) 2539–2561,. URL: https://dl.acm.org/doi/10.5555/1953048.2078187.
[33]
Shi X., Xue S., Wang K., Zhou F., Zhang J.Y., Zhou J., et al., Language models can improve event prediction by few-shot abductive reasoning, 2023,. CoRR abs/2305.16646, arXiv:2305.16646.
[34]
Sun M., Zhou K., He X., Wang Y., Wang X., GPPT: graph pre-training and prompt tuning to generalize graph neural networks, in: Zhang A., Rangwala H. (Eds.), The 28th ACM SIGKDD conference on knowledge discovery and data mining, washington, DC, USA, August 14 - 18, 2022, ACM, 2022, pp. 1717–1727,.
[35]
Tama B.A., Comuzzi M., An empirical comparison of classification techniques for next event prediction using business process event logs, Expert Systems with Applications 129 (2019) 233–245,.
[36]
Waikhom L., Singh Y., Patgiri R., PO-GNN: Position-observant inductive graph neural networks for position-based prediction, Information Processing and Management 60 (3) (2023),.
[37]
Wang Z., Panda R., Karlinsky L., Feris R., Sun H., Kim Y., Multitask prompt tuning enables parameter-efficient transfer learning, in: The eleventh international conference on learning representations, OpenReview.net, 2023, p. 1. URL: https://openreview.net/pdf?id=Nk2pDtuhTq.
[38]
Wang L., Xie J., Zhang X., Huang M., Su H., Zhu J., Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality, in: Oh A., Naumann T., Globerson A., Saenko K., Hardt M., Levine S. (Eds.), Advances in neural information processing systems 36: Annual conference on neural information processing systems 2023, neurIPS 2023, new orleans, la, USA, December 10 - 16, 2023, 2023, p. 1. URL: http://papers.nips.cc/paper_files/paper/2023/hash/d9f8b5abc8e0926539ecbb492af7b2f1-Abstract-Conference.html.
[39]
Wang H., Xiong W., Yu M., Guo X., Chang S., Wang W.Y., Sentence embedding alignment for lifelong relation extraction, in: Burstein J., Doran C., Solorio T. (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, minneapolis, MN, USA, June 2-7, 2019, volume 1 (long and short papers), Association for Computational Linguistics, 2019, pp. 796–806,.
[40]
Wang Z., Zhang Z., Lee C.Y., Zhang H., Sun R., Ren X., et al., Learning to prompt for continual learning, in: IEEE/CVF conference on computer vision and pattern recognition, IEEE, 2022, pp. 139–149,.
[41]
Wen Z., Fang Y., Augmenting low-resource text classification with graph-grounded pre-training and prompting, in: Chen H., Duh W.E., Huang H., Kato M.P., Mothe J., Poblete B. (Eds.), Proceedings of the 46th international ACM SIGIR conference on research and development in information retrieval, SIGIR 2023, taipei, Taiwan, July 23-27, 2023, ACM, 2023, pp. 506–516,.
[42]
Xu Y., Zhang Y., Guo W., Guo H., Tang R., Coates M., GraphSAIL: Graph structure aware incremental learning for recommender systems, in: d’Aquin M., Dietze S., Hauff C., Curry E., Cudré-Mauroux P. (Eds.), The 29th ACM international conference on information and knowledge management, virtual event, Ireland, October 19-23, 2020, ACM, 2020, pp. 2861–2868,.
[43]
Yan Z., Zhou X., Zhang Q., Du R., Ren J., Predicting multi-subsequent events and actors in public health emergencies: An event-based knowledge graph approach, Computers & Industrial Engineering 187 (2024),.
[44]
Yang Y., Wei Z., Chen Q., Wu L., Using external knowledge for financial event prediction based on graph neural networks, in: Zhu W., Tao D., Cheng X., Cui P., Rundensteiner E.A., Carmel D., He Q., Yu J.X. (Eds.), Proceedings of the 28th ACM international conference on information and knowledge management, ACM, 2019, pp. 2161–2164,.
[45]
Yuan Z., Liu H., Hu R., Zhang D., Xiong H., Self-supervised prototype representation learning for event-based corporate profiling, in: Thirty-fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, the eleventh symposium on educational advances in artificial intelligence, EAAI 2021, virtual event, February 2-9, 2021, AAAI Press, 2021, pp. 4644–4652,.
[46]
Zhai P., Yang Y., Zhang C., Causality-based CTR prediction using graph neural networks, Information Processing and Management 60 (1) (2023),.
[47]
Zhang Q., Perra N., Perrotta D., Tizzoni M., Paolotti D., Vespignani A., Forecasting seasonal influenza fusing digital indicators and a mechanistic disease model, in: Barrett R., Cummings R., Agichtein E., Gabrilovich E. (Eds.), Proceedings of the 26th international conference on world wide web, ACM, 2017, pp. 311–319,.
[48]
Zhang X., Song D., Tao D., Cglb: Benchmark tasks for continual graph learning, Advances in Neural Information Processing Systems 35 (2022) 13006–13021.
[49]
Zhang X., Song D., Tao D., Hierarchical prototype networks for continual graph representation learning, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4) (2023) 4622–4636,.
[50]
Zhang Y., Zhai S., Meng Y., Bi S., Chen Y., Qi G., Event is more valuable than you think: Improving the similar legal case retrieval via event knowledge, Information Processing and Management 61 (4) (2024),.
[51]
Zhao L., Event prediction in the big data era: A systematic survey, ACM Computing Surveys 54 (5) (2022) 94:1–94:37,.
[52]
Zhou F., Cao C., Overcoming catastrophic forgetting in graph neural networks with experience replay, in: Thirty-fifth AAAI conference on artificial intelligence, AAAI 2021, thirty-third conference on innovative applications of artificial intelligence, IAAI 2021, the eleventh symposium on educational advances in artificial intelligence, EAAI 2021, virtual event, February 2-9, 2021, AAAI Press, 2021, pp. 4714–4722,.
[53]
Zhu Q., Li B., Mi F., Zhu X., Huang M., Continual prompt tuning for dialog state tracking, in: Muresan S., Nakov P., Villavicencio A. (Eds.), Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long papers), ACL 2022, dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics, 2022, pp. 1124–1137,.
Index Terms
- DST: Continual event prediction by decomposing and synergizing the task commonality and specificity
Index terms have been assigned to the content through auto-classification.
Recommendations
DRAM: A Deep Reinforced Intra-attentive Model for Event Prediction
Knowledge Science, Engineering and ManagementAbstractWe address the problem of event prediction which aims to predict next probable event given a sequence of previous historical events. Event prediction is meaningful and important for the government, agencies and companies to take proactive actions ...
Stream prediction using a generative model based on frequent episodes in event sequences
KDD '08: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data miningThis paper presents a new algorithm for sequence prediction over long categorical event streams. The input to the algorithm is a set of target event types whose occurrences we wish to predict. The algorithm examines windows of events that precede ...
Pred-ID: Future event prediction based on event type schema mining by graph induction and deduction
AbstractIn the field of information management, effective event intelligence management is crucial for its development. With the continuous evolution of events, predicting future events has become a key task in information management. Event Prediction ...
Highlights- Schema Inductive: At each time step, we induce the core event development from the entire instance event graph, to generate an event-type schema.
- Schema Deductive: The event deductive connects induced events to probable types from ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 20 February 2025
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 05 Mar 2025