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Multiplex communities and the emergence of international conflict


Autoři: Caleb Pomeroy aff001;  Niheer Dasandi aff002;  Slava Jankin Mikhaylov aff003
Působiště autorů: Department of Political Science, The Ohio State University, Columbus, Ohio, United States of America aff001;  School of Government, University of Birmingham, Birmingham, United Kingdom aff002;  Data Science Lab, Hertie School, Berlin, Germany aff003
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223040

Souhrn

Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.

Klíčová slova:

Community structure – Democracy – Graphs – Network analysis – Speech signal processing – Telecommunications – Vector spaces – International relations


Zdroje

1. Girvan M, Newman ME. Community structure in social and biological networks. Proceedings of the National Academy of Sciences. 2002;99(12):7821–7826. doi: 10.1073/pnas.122653799

2. Salathé M, Jones JH. Dynamics and control of diseases in networks with community structure. PLOS Computational Biology. 2010;6(4):e1000736. doi: 10.1371/journal.pcbi.1000736 20386735

3. Calcagno V, Demoinet E, Gollner K, Guidi L, Ruths D, de Mazancourt C. Flows of research manuscripts among scientific journals reveal hidden submission patterns. Science. 2012; p. 1227833. doi: 10.1126/science.1227833 23065906

4. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, et al. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015;347(6224):1257601. doi: 10.1126/science.1257601 25700523

5. Lima-Mendez G, Faust K, Henry N, Decelle J, Colin S, Carcillo F, et al. Determinants of community structure in the global plankton interactome. Science. 2015;348(6237):1262073. doi: 10.1126/science.1262073 25999517

6. Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L, Baltier K, et al. Architecture of the human interactome defines protein communities and disease networks. Nature. 2017;545(7655):505. doi: 10.1038/nature22366 28514442

7. Strano E, Viana MP, Sorichetta A, Tatem AJ. Mapping road network communities for guiding disease surveillance and control strategies. Scientific Reports. 2018;8(1):4744. doi: 10.1038/s41598-018-22969-4 29549364

8. Waniek M, Michalak TP, Wooldridge MJ, Rahwan T. Hiding individuals and communities in a social network. Nature Human Behaviour. 2018;2(2):139. doi: 10.1038/s41562-017-0290-3

9. Trujillo CM, Long TM. Document co-citation analysis to enhance transdisciplinary research. Science Advances. 2018;4(1):e1701130. doi: 10.1126/sciadv.1701130 29308433

10. Newman ME, Girvan M. Finding and evaluating community structure in networks. Physical Review E. 2004;69(2):026113. doi: 10.1103/PhysRevE.69.026113

11. Duch J, Arenas A. Community detection in complex networks using extremal optimization. Physical Review E. 2005;72(2):027104. doi: 10.1103/PhysRevE.72.027104

12. Newman ME. Modularity and community structure in networks. Proceedings of the National Academy of Sciences. 2006;103(23):8577–8582. doi: 10.1073/pnas.0601602103

13. Traag VA, Bruggeman J. Community detection in networks with positive and negative links. Physical Review E. 2009;80(3):036115. doi: 10.1103/PhysRevE.80.036115

14. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela JP. Community structure in time-dependent, multiscale, and multiplex networks. Science. 2010;328(5980):876–878. doi: 10.1126/science.1184819 20466926

15. Benson AR, Gleich DF, Leskovec J. Higher-order organization of complex networks. Science. 2016;353(6295):163–166. doi: 10.1126/science.aad9029 27387949

16. Su Y, Wang B, Cheng F, Zhang L, Zhang X, Pan L. An algorithm based on positive and negative links for community detection in signed networks. Scientific Reports. 2017;7(1):10874. doi: 10.1038/s41598-017-11463-y 28883663

17. Wilson JD, Palowitch J, Bhamidi S, Nobel AB. Community Extraction in Multilayer Networks with Heterogeneous Community Structure. Journal of Machine Learning Research. 2017;18(149):1–49.

18. Zhai X, Zhou W, Fei G, Liu W, Xu Z, Jiao C, et al. Null Model and Community Structure in Multiplex Networks. Scientific Reports. 2018;8(1):3245. doi: 10.1038/s41598-018-21286-0 29459696

19. Hoffmann S. Rousseau on war and peace. American Political Science Review. 1963;57(2):317–333. doi: 10.2307/1952825

20. Kenneth W. Theory of international politics. Addison-Wesley; 1979.

21. Linklater A. Global civilizing processes and the ambiguities of human interconnectedness. European Journal of International Relations. 2010;16(2):155–178. doi: 10.1177/1354066109350796

22. Doyle MW. Liberalism and world politics. American Political Science Review. 1986;80(4):1151–1169.

23. Oneal JR, Russett B. The Kantian peace: The pacific benefits of democracy, interdependence, and international organizations, 1885–1992. World Politics. 1999;52(1):1–37. doi: 10.1017/S0043887100020013

24. Barbieri K. Economic interdependence: A path to peace or a source of interstate conflict? Journal of Peace Research. 1996;33(1):29–49. doi: 10.1177/0022343396033001003

25. Oneal JR, Oneal FH, Maoz Z, Russett B. The liberal peace: Interdependence, democracy, and international conflict, 1950-85. Journal of Peace Research. 1996;33(1):11–28. doi: 10.1177/0022343396033001002

26. Poast P. (Mis)Using dyadic data to analyze multilateral events. Political Analysis. 2010;18(4):403–425. doi: 10.1093/pan/mpq024

27. Lupu Y, Traag VA. Trading communities, the networked structure of international relations, and the Kantian peace. Journal of Conflict Resolution. 2013;57(6):1011–1042. doi: 10.1177/0022002712453708

28. McMillan SM. Interdependence and conflict. Mershon International Studies Review. 1997;41(Supplement_1):33–58. doi: 10.2307/222802

29. Cranmer SJ, Menninga EJ, Mucha PJ. Kantian fractionalization predicts the conflict propensity of the international system. Proceedings of the National Academy of Sciences. 2015;112(38):11812–11816. doi: 10.1073/pnas.1509423112

30. Pauls SD, Cranmer SJ. Affinity communities in United Nations voting: Implications for democracy, cooperation, and conflict. Physica A: Statistical Mechanics and its Applications. 2017;484:428–439. doi: 10.1016/j.physa.2017.04.177

31. Lozano S, Arenas A, Sanchez A. Mesoscopic structure conditions the emergence of cooperation on social networks. PLoS ONE. 2008;3(4):e1892. doi: 10.1371/journal.pone.0001892 18382673

32. Gianetto DA, Heydari B. Network modularity is essential for evolution of cooperation under uncertainty. Scientific Reports. 2015;5:9340. doi: 10.1038/srep09340 25849737

33. Gómez-Gardenes J, Reinares I, Arenas A, Floría LM. Evolution of cooperation in multiplex networks. Scientific Reports. 2012;2:620. doi: 10.1038/srep00620 22943006

34. Wang Z, Szolnoki A, Perc M. Evolution of public cooperation on interdependent networks: The impact of biased utility functions. EPL (Europhysics Letters). 2012;97(4):48001.

35. Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. Journal of Complex Networks. 2014;2(3):203–271. doi: 10.1093/comnet/cnu016

36. Aleta A, Moreno Y. Multilayer networks in a nutshell. Annual Review of Condensed Matter Physics. 2018.

37. Porter MA. What is… a Multilayer Network? Notices of the AMS. 2018;65(11).

38. Pearson GJ. Rohn’s World Treaty Index: Its Past and Future. International Journal of Legal Information. 2001;29:543. doi: 10.1017/S0731126500001025

39. Poast P, Bommarito MJ, Katz DM. The Electronic World Treaty Index: Collecting the Population of International Agreements in the 20th Century; 2010.

40. Kinne BJ. Network dynamics and the evolution of international cooperation. American Political Science Review. 2013;107(4):766–785. doi: 10.1017/S0003055413000440

41. Krasner SD. Global communications and national power: Life on the Pareto frontier. World Politics. 1991;43(3):336–366. doi: 10.2307/2010398

42. Morrow JD. Modeling the forms of international cooperation: distribution versus information. International Organization. 1994;48(3):387–423. doi: 10.1017/S0020818300028241

43. Haas EB. Why collaborate? Issue-linkage and international regimes. World Politics. 1980;32(3):357–405. doi: 10.2307/2010109

44. Baturo A, Dasandi N, Jankin Mikhaylov S. Understanding State Preferences with Text As Data: Introducing the UN General Debate Corpus. Research and Politics. 2017;4(2):1–9. doi: 10.1177/2053168017712821

45. Voeten E. Clashes in the Assembly. International Organization. 2000;54(2):185–215. doi: 10.1162/002081800551154

46. Voeten E. Resisting the lonely superpower: Responses of states in the United Nations to US dominance. Journal of Politics. 2004;66(3):729–754. doi: 10.1111/j.1468-2508.2004.00274.x

47. Voeten E. Data and Analyses of Voting in the United Nations General Assembly. In: Reinalda B, editor. Routledge Handbook of International Organization. Routledge; 2013. p. 54.

48. Bailey MA, Strezhnev A, Voeten E. Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution. 2017;61(2):430–456. doi: 10.1177/0022002715595700

49. Macon KT, Mucha PJ, Porter MA. Community structure in the united nations general assembly. Physica A: Statistical Mechanics and its Applications. 2012;391(1-2):343–361. doi: 10.1016/j.physa.2011.06.030

50. Lauderdale BE, Clark TS. Scaling politically meaningful dimensions using texts and votes. American Journal of Political Science. 2014;58(3):754–771. doi: 10.1111/ajps.12085

51. Kim IS, Londregan J, Ratkovic M. Estimating Spatial Preferences from Votes and Text. Political Analysis. 2018;26(2):210–229. doi: 10.1017/pan.2018.7

52. Peterson A, Spirling A. Classification Accuracy as a Substantive Quantity of Interest: Measuring Polarization in Westminster Systems. Political Analysis. 2018;26(1):120–128. doi: 10.1017/pan.2017.39

53. Lauretig AM. Identification, Interpretability, and Bayesian Word Embeddings. arXiv preprint arXiv:190401628. 2019.

54. Pennington J, Socher R, Manning CD. GloVe: Global Vectors for Word Representation. In: Empirical Methods in Natural Language Processing (EMNLP). vol. 14; 2014. p. 1532–1543.

55. Kusner M, Sun Y, Kolkin N, Weinberger K. From word embeddings to document distances. In: International Conference on Machine Learning; 2015. p. 957–966.

56. Ozaki K, Shimbo M, Komachi M, Matsumoto Y. Using the mutual k-nearest neighbor graphs for semi-supervised classification of natural language data. In: Proceedings of the fifteenth conference on computational natural language learning. Association for Computational Linguistics; 2011. p. 154–162.

57. Jenke L, Gelpi C. Theme and variations: Historical contingencies in the causal model of interstate conflict. Journal of Conflict Resolution. 2017;61(10):2262–2284. doi: 10.1177/0022002715615190

58. Bueno de Mesquita B. Systemic polarization and the occurrence and duration of war. Journal of Conflict Resolution. 1978;22(2):241–267. doi: 10.1177/002200277802200203

59. Cranmer SJ, Desmarais BA, Menninga EJ. Complex dependencies in the alliance network. Conflict Management and Peace Science. 2012;29(3):279–313. doi: 10.1177/0738894212443446

60. Robins G, Pattison P. Random graph models for temporal processes in social networks. Journal of Mathematical Sociology. 2001;25(1):5–41. doi: 10.1080/0022250X.2001.9990243

61. Hanneke S, Fu W, Xing EP, et al. Discrete temporal models of social networks. Electronic Journal of Statistics. 2010;4:585–605. doi: 10.1214/09-EJS548

62. Wasserman S, Pattison P. Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp. Psychometrika. 1996;61(3):401–425. doi: 10.1007/BF02294547

63. Pinker S. The better angels of our nature: Why violence has declined. Penguin Group USA; 2012.

64. Bueno de Mesquita B. The war trap. New Haven, CT: Yale University Press; 1981.

65. Gibler DM, Vasquez JA. Uncovering the dangerous alliances, 1495–1980. International Studies Quarterly. 1998;42(4):785–807. doi: 10.1111/0020-8833.00106

66. Bremer SA. Dangerous dyads: Conditions affecting the likelihood of interstate war, 1816-1965. Journal of Conflict Resolution. 1992;36(2):309–341. doi: 10.1177/0022002792036002005

67. Braithwaite A. Location, location, location… identifying hot spots of international conflict. International Interactions. 2005;31(3):251–273. doi: 10.1080/03050620500294234

68. Zhukov YM, Stewart BM. Choosing your neighbors: Networks of diffusion in international relations. International Studies Quarterly. 2013;57(2):271–287. doi: 10.1111/isqu.12008

69. Nowak MA. Five rules for the evolution of cooperation. Science. 2006;314(5805):1560–1563. doi: 10.1126/science.1133755 17158317

70. Kasten L. When less is more: Constructing a parsimonious concept of interstate peace for quantitative analysis. International Studies Review. 2017;19(1):28–52. doi: 10.1093/isr/vix002

71. Yildirim MA, Coscia M. Using random walks to generate associations between objects. PLoS ONE. 2014;9(8):e104813. doi: 10.1371/journal.pone.0104813 25153830

72. Serrano MÁ, Boguná M, Vespignani A. Extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences. 2009;106(16):6483–6488. doi: 10.1073/pnas.0808904106

73. Slater PB. A two-stage algorithm for extracting the multiscale backbone of complex weighted networks. Proceedings of the National Academy of Sciences. 2009;106(26):E66–E66. doi: 10.1073/pnas.0904725106

74. Zhang X, Zhang Z, Zhao H, Wang Q, Zhu J. Extracting the globally and locally adaptive backbone of complex networks. PLoS ONE. 2014;9(6):e100428. doi: 10.1371/journal.pone.0100428 24936975

75. Palmer G, d’Orazio V, Kenwick M, Lane M. The MID4 dataset, 2002–2010: Procedures, coding rules and description. Conflict Management and Peace Science. 2015;32(2):222–242. doi: 10.1177/0738894214559680

76. Benoit K, Nulty P. quanteda: Quantitative Analysis of Textual Data; 2013.

77. Selivanov D. text2vec: Modern Text Mining Framework for R; 2016.

78. R Core Team. R: A Language and Environment for Statistical Computing; 2017. Available from: https://www.R-project.org.

79. Cranmer SJ, Desmarais BA. Inferential network analysis with exponential random graph models. Political Analysis. 2010;19(1):66–86. doi: 10.1093/pan/mpq037

80. Leifeld P, Schneider V. Information exchange in policy networks. American Journal of Political Science. 2012;56(3):731–744. doi: 10.1111/j.1540-5907.2011.00580.x

81. Almquist ZW, Butts CT. Dynamic network logistic regression: A logistic choice analysis of inter-and intra-group blog citation dynamics in the 2004 US presidential election. Political Analysis. 2013;21(4):430–448. doi: 10.1093/pan/mpt016 24143060

82. Desmarais BA, Cranmer SJ. Consistent confidence intervals for maximum pseudolikelihood estimators. In: Proceedings of the Neural Information Processing Systems 2010 Workshop on Computational Social Science and the Wisdom of Crowds. Citeseer; 2010.

83. Desmarais BA, Cranmer SJ. Statistical mechanics of networks: Estimation and uncertainty. Physica A: Statistical Mechanics and its Applications. 2012;391(4):1865–1876. doi: 10.1016/j.physa.2011.10.018

84. Leifeld P, Cranmer S, Desmarais B. Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software. 2018;83(6):1–36. doi: 10.18637/jss.v083.i06

85. Desmarais BA, Cranmer SJ. Micro-level interpretation of exponential random graph models with application to estuary networks. Policy Studies Journal. 2012;40(3):402–434. doi: 10.1111/j.1541-0072.2012.00459.x

86. Snijders TA, Pattison PE, Robins GL, Handcock MS. New specifications for exponential random graph models. Sociological Methodology. 2006;36(1):99–153. doi: 10.1111/j.1467-9531.2006.00176.x


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