{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T16:59:09Z","timestamp":1773939549679,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T00:00:00Z","timestamp":1773878400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Postdoctoral Project of Hubei Province","award":["2024HBBHCXA024"],"award-info":[{"award-number":["2024HBBHCXA024"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62402187"],"award-info":[{"award-number":["62402187"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012456","name":"National Social Science Foundation of China","doi-asserted-by":"publisher","award":["23BXW063"],"award-info":[{"award-number":["23BXW063"]}],"id":[{"id":"10.13039\/501100012456","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024M751009"],"award-info":[{"award-number":["2024M751009"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZB20240243"],"award-info":[{"award-number":["GZB20240243"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>On 12 July 2016, the ruling on the South China Sea Arbitration was announced and rapidly drew worldwide attention, turning the event into a major international hotspot. Quantifying the dynamics of such hotspot events and understanding the evolution of media-based inter-actor networks during major shocks are of substantial research interest. Viewing these interactions as dynamic networks, we analyze the time-varying actor interaction structure surrounding the arbitration using the Global Database of Events, Location and Tone (GDELT), a large-scale media-based event database with global coverage since 1979. We extract nearly 30,000 events related to the arbitration from 5 July to 25 July 2016, constructing daily cooperation and conflict networks to quantify structural changes via network size and degree-entropy dynamics. To further reveal actor-level structural roles, we learn node embeddings on each daily network via an entropy-driven graph representation learning scheme and perform embedding-based clustering with automatically selected cluster numbers, visualized via t-SNE. The results show that key dates in the event window are associated with pronounced structural shifts in the networks, including changes in participation breadth, degree-distribution heterogeneity, and clearer differentiation and reconfiguration of actor roles, with distinct patterns between cooperation and conflict networks. These findings demonstrate the potential of massive media event data for characterizing structural responses and actor-role evolution in event-driven inter-actor networks.<\/jats:p>","DOI":"10.3390\/e28030347","type":"journal-article","created":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:38:01Z","timestamp":1773934681000},"page":"347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Characterizing the Evolution of Inter-Actor Networks in the South China Sea Arbitration via Entropy-Driven Graph Representation Learning from Massive Media Event Data"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7013-0708","authenticated-orcid":false,"given":"Menglan","family":"Ma","sequence":"first","affiliation":[{"name":"School of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7735-2766","authenticated-orcid":false,"given":"Hong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Journalism and Information Communication, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4741-9282","authenticated-orcid":false,"given":"Peng","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1080\/713613514","article-title":"The South China Sea Dispute Revisited","volume":"54","author":"Guan","year":"2000","journal-title":"Aust. J. Int. Aff."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00908320.2022.2034555","article-title":"Managing the South China Sea dispute: Multilateral and bilateral approaches","volume":"53","author":"Peng","year":"2022","journal-title":"Ocean. Dev. Int. Law"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"532","DOI":"10.2307\/2600291","article-title":"Foreign policy makers and their national role conceptions","volume":"24","author":"Wish","year":"1980","journal-title":"Int. Stud. Q."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"155","DOI":"10.14254\/2071-8330.2021\/14-3\/10","article-title":"The network analysis of international relations: Overview of an emergent methodology","volume":"14","author":"Kacziba","year":"2021","journal-title":"J. Int. Stud."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6902","DOI":"10.1109\/TKDE.2025.3618389","article-title":"An event-centric framework for predicting crime hotspots with flexible time intervals","volume":"37","author":"Jin","year":"2025","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_6","first-page":"1","article-title":"Representation Learning for Dynamic Graphs: A Survey","volume":"21","author":"Kazemi","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1109\/TIT.2023.3329617","article-title":"Information-theoretic characterizations of generalization error for the Gibbs algorithm","volume":"70","author":"Aminian","year":"2023","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation learning: A review and new perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Chen, P., Jatowt, A., and Yoshikawa, M. (April, January 30). Conflict or cooperation? Predicting future tendency of international relations. Proceedings of the 35th Annual ACM Symposium on Applied Computing, Brno, Czech Republic."},{"key":"ref_10","unstructured":"Schein, A., Paisley, J., Blei, D.M., and Wallach, H. (2014, January 12\u201313). Inferring polyadic events with Poisson tensor factorization. Proceedings of the NIPS 2014 Workshop on Networks, Montreal, QC, Canada."},{"key":"ref_11","unstructured":"Bi, S., Gao, J., Wang, Y., and Cao, Y. (2015). A contrast of the degree of activity among the three major powers, USA, China, and Russia: Insights from media reports. Proceedings of the 2015 International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Keneshloo, Y., Cadena, J., Korkmaz, G., and Ramakrishnan, N. (2014, January 23\u201326). Detecting and forecasting domestic political crises: A graph-based approach. Proceedings of the 2014 ACM Conference on Web Science, Bloomington, IN, USA.","DOI":"10.1145\/2615569.2615698"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.spl.2014.06.015","article-title":"Bayesian dynamic financial networks with time-varying predictors","volume":"93","author":"Durante","year":"2014","journal-title":"Stat. Probab. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kwak, H., and An, J. (2014). A first look at global news coverage of disasters by using the GDELT dataset. Social Informatics, Springer.","DOI":"10.1007\/978-3-319-13734-6_22"},{"key":"ref_15","unstructured":"Yonamine, J. (2013). A Nuanced Study of Political Conflict Using the Global Datasets of Events Location and Tone (GDELT) Dataset. [Master\u2019s Thesis, The Pennsylvania State University]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Fang, P., Gao, J., Fan, F., and Yang, L. (2016). Identifying political \u201chot\u201d spots through massive media data analysis. Proceedings of the International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation, Springer.","DOI":"10.1007\/978-3-319-39931-7_27"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ma, M., Fang, P., Gao, J., and Song, C. (2017). Does ideology affect the tone of international news coverage?. Proceedings of the 2017 International Conference on Behavioral, Economic, Socio-Cultural Computing (BESC), IEEE.","DOI":"10.1109\/BESC.2017.8256368"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hong, D., Fu, Z., Zhang, X., and Pan, Y. (2025). Research on the Development and Application of the GDELT Event Database. Data, 10.","DOI":"10.3390\/data10100158"},{"key":"ref_19","first-page":"18","article-title":"On random graphs I","volume":"6","author":"ERDdS","year":"1959","journal-title":"Publ. Math. Debrecen"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/30918","article-title":"Collective dynamics of \u2018small-world\u2019networks","volume":"393","author":"Watts","year":"1998","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1126\/science.286.5439.509","article-title":"Emergence of scaling in random networks","volume":"286","author":"Albert","year":"1999","journal-title":"Science"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1177\/0038038507087353","article-title":"Small-world networks, complex systems and sociology","volume":"42","author":"Crossley","year":"2008","journal-title":"Sociology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1209\/0295-5075\/99\/39001","article-title":"Dynamical evolution of the community structure of complex earthquake network","volume":"99","author":"Abe","year":"2012","journal-title":"Europhys. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, W., Wang, W., Gao, J., and Li, Q. (2015). Dynamical evolution of an internet social network: A case study on an event of protecting plane trees in Nanjing, China. Proceedings of the International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC), IEEE.","DOI":"10.1109\/BESC.2015.7365950"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1616","DOI":"10.1109\/TKDE.2018.2807452","article-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","volume":"30","author":"Cai","year":"2018","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hamilton, W.L. (2020). Graph Representation Learning, Morgan & Claypool Publishers.","DOI":"10.1007\/978-3-031-01588-5"},{"key":"ref_27","first-page":"47133","article-title":"Learning Graph Representation via Graph Entropy Maximization","volume":"Volume 235","author":"Sun","year":"2024","journal-title":"Proceedings of the 41st International Conference on Machine Learning (ICML)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fang, P., Wang, F., Shi, Z., Jiang, H., Feng, D., and Yang, L. (2021). HuGE: An entropy-driven approach to efficient and scalable graph embeddings. Proceedings of the 2021 IEEE 37th International Conference on Data Engineering (ICDE), IEEE.","DOI":"10.1109\/ICDE51399.2021.00198"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/TBDATA.2022.3164575","article-title":"How to realize efficient and scalable graph embeddings via an entropy-driven mechanism","volume":"9","author":"Fang","year":"2022","journal-title":"IEEE Trans. Big Data"},{"key":"ref_30","unstructured":"Wu, J., Chen, X., Xu, K., and Li, S. (2022). Structural entropy guided graph hierarchical pooling. Proceedings of the 39th International Conference on Machine Learning (ICML), PMLR."},{"key":"ref_31","unstructured":"Wu, J., Chen, X., Shi, B., Li, S., and Xu, K. (2023). SEGA: Structural entropy guided anchor view for graph contrastive learning. Proceedings of the 40th International Conference on Machine Learning (ICML), PMLR."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1177\/0022002792036002007","article-title":"A conflict-cooperation scale for WEIS events data","volume":"36","author":"Goldstein","year":"1992","journal-title":"J. Confl. Resolut."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1109\/TKDE.2018.2833443","article-title":"Heterogeneous Information Network Embedding for Recommendation","volume":"31","author":"Shi","year":"2019","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wei, X., Xu, L., Cao, B., and Yu, P.S. (2017, January 3\u20137). Cross View Link Prediction by Learning Noise-resilient Representation Consensus. Proceedings of the WWW, Perth, Australia.","DOI":"10.1145\/3038912.3052575"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.C. (2011). Node Classification in Social Networks. Social Network Data Analytics, Springer.","DOI":"10.1007\/978-1-4419-8462-3"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Grover, A., and Leskovec, J. (2016, January 13\u201317). Node2vec: Scalable Feature Learning for Networks. Proceedings of the KDD, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939754"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Perozzi, B., Al-Rfou, R., and Skiena, S. (2014, January 24\u201327). DeepWalk: Online Learning of Social Representations. Proceedings of the KDD, New York, NY, USA.","DOI":"10.1145\/2623330.2623732"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., and Mei, Q. (2015, January 18\u201322). LINE: Large-scale Information Network Embedding. Proceedings of the WWW, Florence, Italy.","DOI":"10.1145\/2736277.2741093"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, Z., Lin, S., Xie, H., Lv, M., Xu, Y., and Lui, J.C.S. (2019, January 8\u201312). Walking with Perception: Efficient Random Walk Sampling via Common Neighbor Awareness. Proceedings of the ICDE, Macau, China.","DOI":"10.1109\/ICDE.2019.00090"},{"key":"ref_40","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., and Dean, J. (2013, January 5\u201310). Distributed Representations of Words and Phrases and their Compositionality. Proceedings of the NeurIPS, Lake Tahoe, NV, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/S0031-3203(02)00060-2","article-title":"The global k-means clustering algorithm","volume":"36","author":"Likas","year":"2003","journal-title":"Pattern Recognit."},{"key":"ref_42","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Maaten","year":"2008","journal-title":"J. Mach. Learn. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/3\/347\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T15:41:20Z","timestamp":1773934880000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/28\/3\/347"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,19]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["e28030347"],"URL":"https:\/\/doi.org\/10.3390\/e28030347","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,19]]}}}