Conflict forecasting with event data and spatio-temporal graph convolutional networks

References

• Beck, N., G. King, and L. Zeng. 2000. “Improving Quantitative Studies of International Conflict: A Conjecture.” American Political Science Review 94 (1): 21–35. doi:10.1017/S0003055400220078.
   Web of Science ®Google Scholar
• Berman, N., M. Couttenier, D. Rohner, and M. Thoenig. 2017. “This Mine Is Mine! How Minerals Fuel Conflicts in Africa.” American Economic Review 107 (6): 1564–1610. doi:10.1257/aer.20150774.
   Web of Science ®Google Scholar
• Binkowski, M., G. Marti, and P. Donnat. 2018. “Autoregressive Convolutional Neural Networks for Asynchronous Time Series.” In Vol. 80 of the Proceedings of the International Conference on Machine Learning, edited by Jennifer Dy and Andreas Krause, 580–589. Stockholm: PMLR.
   Google Scholar
• Blair, R. A., and N. Sambanis. 2020. “Forecasting Civil Wars: Theory and Structure in an Age of ‘Big Data’ and Machine Learning.” Journal of Conflict Resolution 64 (10): 1885–1915. doi:10.1177/0022002720918923.
   Web of Science ®Google Scholar
• Blake, J. 2020. “Preventing the Next Boko Haram in Northern Mozambique.” Council on Foreign Relations, May 20. Accessed 14 December 2020. https://www.cfr.org/blog/preventingnext-boko-haram-northern-mozambique
   Google Scholar
• Boschee, E., J. Lautenschlager, S. O’Brien, S. Shellman, J. Starz, and M. Ward. 2015. “ICEWS” Coded Event Data.” Harvard Dataverse V30. Accessed 20 February 2022. doi:10.7910/DVN/28075.
   Google Scholar
• Brandt, P. T., J. R. Freeman, and P. A. Schrodt. 2014. “Evaluating Forecasts of Political Conflict Dynamics.” International Journal of Forecasting 30 (4): 944–962. doi:10.1016/j.ijforecast.2014.03.014.
   Web of Science ®Google Scholar
• Buhaug, H., and K. S. Gleditsch. 2008. “Contagion or Confusion? Why Conflicts Cluster in Space.” International Studies Quarterly 52 (2): 215–233. doi:10.1111/j.1468-2478.2008.00499.x.
   Web of Science ®Google Scholar
• Campbell, J. 2020. “The Military-First Approach in Northern Mozambique Is Bound to Fail.” Accessed 14 December 2020. https://www.cfr.org/blog/military-first-approach-northern-mozambique-bound-fail.
   Google Scholar
• Chiba, D., and K. S. Gleditsch. 2017. “The Shape of Things to Come? Expanding the Inequality and Grievance Model for Civil War Forecasts with Event Data.” Journal of Peace Research 54 (2): 275–297. doi:10.1177/0022343316684192.
   Web of Science ®Google Scholar
• Cranmer, S. J., and B. A. Desmarais. 2017. “What Can We Learn from Predictive Modeling?” Political Analysis 25 (2): 145–166. doi:10.1017/pan.2017.3.
   Web of Science ®Google Scholar
• Deng, L., and Y. Liu. 2018. Deep Learning in Natural Language Processing. New York: Springer.
   Google Scholar
• D’Orazio, V. 2020. “Conflict Forecasting and Prediction.” Oxford Research Encyclopedia of International Studies. doi:10.1093/acrefore/9780190846626.013.514.
   Google Scholar
• D’Orazio, V., and Y. Lin. 2022. “Forecasting Conflict in Africa with Automated Machine Learning Systems.” International Interactions 48 (4). doi:10.1080/03050629.2022.2017290
   Web of Science ®Google Scholar
• Dorff, C., M. Gallop, and S. Minhas. 2020. “Networks of Violence: Predicting Conflict in N”Igeria.” The Journal of Politics 82 (2): 476–493. doi:10.1086/706459.
   Web of Science ®Google Scholar
• Findley, M. G., and J. K. Young. 2012. “Terrorism and Civil War: A Spatial and Temporal Approach to a Conceptual Problem.” Perspectives on Politics 10 (2): 285–305. doi:10.1017/S1537592712000679.
   Web of Science ®Google Scholar
• Frazier, P. I. 2018. “A Tutorial on Bayesian Optimization.” arXiv preprint arXiv:1807.02811.
   Google Scholar
• Gerner, D. J., P. A. Schrodt, and Ö. Yilmaz. 2009. “Conflict and Mediation Event Observations (CAMEO): An Event Data Framework for a Post Cold War World.” In International Conflict Mediation: New Approaches and Findings, edited by J. Bercovitch and S. Gartner, 287–304. New York: Routledge.
   Google Scholar
• Guo, S., Y. Lin, N. Feng, C. Song, and H. Wan. 2019. “Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting.” I Vol. 33 of the Proceedings of the AAAI Conference on Artificial Intelligence, 922–929. Palo Alto: AAAI Press.
   Google Scholar
• Hegre, H., C. Bell, M. Colaresi, M. Croicu, F. Hoyles, R. Jansen, M. R. Leis, et al. 2021. “ViEWS2020: Revising and Evaluating the ViEWS Political Violence Early-Warning System.” Journal of Peace Research 58 (3): 599–611. doi:10.1177/0022343320962157.
   Web of Science ®Google Scholar
• Hegre, H., M. Allansson, M. Basedau, M. Colaresi, M. Croicu, H. Fjelde, F. Hoyles, et al. 2019. ‘ViEWS’: A Political Violence Early-Warning System.” Journal of Peace Research 56 (2): 155–174. doi:10.1177/0022343319823860.
   Web of Science ®Google Scholar
• Hegre, Håvard, Paola Vesco, and Michael Colaresi. 2022. “Lessons from an Escalation Prediction Competition.” International Interactions. 48 (4).
   Web of Science ®Google Scholar
• Hersbach, H. 2000. “Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems.” Weather and Forecasting 15 (5): 559–570. doi:10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2.
   Web of Science ®Google Scholar
• Hill, D. W., and Z. M. Jones. 2014. “An Empirical Evaluation of Explanations for State Repression.” American Political Science Review 108 (3): 661–687. doi:10.1017/S0003055414000306.
   Web of Science ®Google Scholar
• Kane, M. J., N. Price, M. Scotch, and P. Rabinowitz. 2014. “Comparison of ARIMA and Random Forest Time Series Models for Prediction of Avian Influenza H5N1 Outbreaks.” BMC Bioinformatics 15 (1): 1–9. doi:10.1186/1471-2105-15-276.
   PubMed Web of Science ®Google Scholar
• Kim, H., V. D’Orazio, P. T. Brandt, J. Looper, S. Salam, L. Khan, and M. Shoemate. 2019. “UTD”Eventdata: An R Package to Access Political Event Data.” Journal of Open Source Software 4 (36): 1322. doi:10.21105/joss.01322.
   Google Scholar
• Kipf, T. N., and M. Welling. 2017. “Semi-Supervised Classification with Graph Convolutional Networks.” Paper presented at the 5th International Conference on Learning Representations, ICLR 2017, Toulon, April 24–26.
   Google Scholar
• Lea C., Vidal R., Reiter A., Hager G.D. 2016. “Temporal Convolutional Networks: A Unified Approach to Action Segmentation.” In: Computer Vision – ECCV 2016 Workshops, edited by G. Hu, and H. Jégou. ECCV 2016. Cham: Springer.
   Google Scholar
• Li, Y., K. Fu, Z. Wang, C. Shahabi, J. Ye, and Y. Liu. 2018. “Multi-Task Representation Learning for Travel Time Estimation.” Paper presented at the Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, August 19-23.
   Google Scholar
• Li, Y., R. Yu, C. Shahabi, and Y. Liu. 2018. “Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.” International Conference on Learning Representations.
   Google Scholar
• Lim, B., and S. Zohren. 2020. “Time Series Forecasting With Deep Learning: A Survey.” arXiv preprint arXiv:2004.13408.
   Google Scholar
• McClelland, C. A. 1976. “World Event/Interaction Survey Codebook (ICPSR 5211).”
   Google Scholar
• Parolin, E. S., L. Khan, J. Osorio, V. D’Orazio, P. T. Brandt, and J. Holmes. 2020. “HANKE: Hierarchical Attention Networks for Knowledge Extraction in Political Science Domain.” Paper presented at the 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), Sydney, October 6-9.
   Google Scholar
• Schrodt, P. A. 2012. “Precedents, Progress, and Prospects in Political Event Data.” International Interactions 38 (4): 546–569. doi:10.1080/03050629.2012.697430.
   Web of Science ®Google Scholar
• Seo, Y., M. Defferrard, P. Vandergheynst, and X. Bresson. 2018. “Structured Sequence Modeling with Graph Convolutional Recurrent Networks.” International Conference on Neural Information Processing, 362–373. Springer.
   Google Scholar
• Siami-Namini, S., N. Tavakoli, and A. S. Namin. 2018. “A Comparison of ARIMA and LSTM in Forecasting Time Series.” 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), 1394–1401. IEEE. doi:10.1109/ICMLA.2018.00227.
   Google Scholar
• Tollefsen, A. F., H. Strand, and H. Buhaug. 2012. “PRIO-GRID: A Unified Spatial Data Structure.” Journal of Peace Research 49 (2): 363–374. doi:10.1177/0022343311431287.
   Web of Science ®Google Scholar
• Tollefsen, A. F., K. Bahgat, J. Nordkvelle, and H. Buhaug. 2015. “PRIO-GRID v.2.0 Codebook.”
   Google Scholar
• Vesco, Paola, H. Hegre, M. Colaresi, B. J. Remco, A. Lo, G. Reisch, and N. Weidmann. 2022. “United They Stand: Findings from an Escalation Prediction Competition.” International Interactions 48 (4). doi:10.1080/03050629.2022.2029856
   Web of Science ®Google Scholar
• Wang, X., Y. Ma, Y. Wang, W. Jin, X. Wang, J. Tang, C. Jia, and J. Yu. 2020. “Traffic Flow Prediction via Spatial Temporal Graph Neural Network.” Proceedings of the Web Conference 2020, 1082–1092.
   Google Scholar
• Ward, M. D., B. D. Greenhill, and K. M. Bakke. 2010. “The Perils of Policy by p-Value: Predicting Civil Conflicts.” Journal of Peace Research 47 (4): 363–375. doi:10.1177/0022343309356491.
   Web of Science ®Google Scholar
• Wu, Z., S. Pan, G. Long, J. Jiang, and C. Zhang. 2019. “Graph Wavenet for Deep Spatial-Temporal Graph Modeling.” Proceedings of the 28th International Joint Conference on Artificial Intelligence, 1907–1913. AAAI Press.
   Google Scholar
• Yan, S., Y. Xiong, and D. Lin. 2018. “Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition.” arXiv preprint arXiv:1801.07455.
   Google Scholar
• Yu, B., H. Yin, and Z. Zhu. 2018. “Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting.” In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018, July 13-19, 2018, Stockholm, Sweden, edited by J. Lang, 3634–3640. ijcai.org.
   Google Scholar
• Zhang, J., X. Shi, J. Xie, H. Ma, I. King, and D.-Y. Yeung. 2018. “Gaan: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs.” arXiv preprint arXiv:1803.07294.
   Google Scholar