The nuclear network: multiplex network analysis for interconnected systems

Abstract

States facing the decision to develop a nuclear weapons program do so within a broader context of their relationships with other countries. How these diplomatic, economic, and strategic relationships impact proliferation decisions, however, remains under-specified. Adding to the existing empirical literature that attempts to model state proliferation decisions, this article introduces the first quantitative heterogeneous network analysis of how networks of conflict, alliances, trade, and nuclear cooperation interact to spur or deter nuclear proliferation. Using a multiplex network model, we conceptualize states as nodes linked by different modes of interaction represented on individual network layers. Node strength is used to quantify factors correlated with nuclear proliferation and these are combined in a weighted sum across layers to provide a metric characterizing the proliferation behavior of the state. This multiplex network modeling approach provides a means for identifying states with the highest relative likelihood of proliferation-based only on their relationships to other states. This work demonstrates that latent conflict and nuclear cooperation are positively correlated with proliferation, while an increased trade dependence suggests a decreased proliferation likelihood. A case study on Iran's controversial nuclear program and past nuclear activity is also provided. These findings have clear, policy-relevant conclusions related to alliance posture, sanctions policy, and nuclear assistance.

Evolution of cooperation in multiplex networks: Cases in which asymmetry between interaction and replacement promotes cooperation

“…While many network reciprocity studies assume that interactions and strategy replacements are constrained to a single network, we address the evolution of cooperation in two-layer multiplex networks 3,[15][16][17][18][19][20] , where interactions and replacements are constrained to two different networks. A multiplex network is a network consisting of multiple layers, where nodes are common to all layers but edges are connected in different ways in each layer.…”

Bias-Variance Tradeoffs in Joint Spectral Embeddings

“…Multiplex networks describe a set of entities, with multiple types of relationships among them, as a collection of networks over a common vertex set (Kivelä et al 2014;Battiston, Nicosia, and Latora 2017). There is a growing demand for inferential frameworks for multiplex networks in a diverse variety of fields such as neuroscience (Battiston, Nicosia, Chavez, et al 2016;De Domenico 2017;Ginestet et al 2017), transportation systems (Cardillo et al 2013;Kaluza et al 2010), and the social sciences (Coscia et al 2013;Goldblum et al 2019;Takes et al 2018;Szell and Thurner 2013;Stella, Beckage, and Brede 2017;Lazega and Snijders 2016). While developing a principled paradigm for random graph inference has been of great interest for individual networks (Kolaczyk 2009;Avanti Athreya et al 2017), lesser attention has been given to multiplex networks.…”

Evolution of cooperation in multiplex networks through asymmetry between interaction and replacement

“…While many network reciprocity studies assume that interactions and strategy replacements are constrained to a single network, we address the evolution of cooperation in two-layer multiplex networks 3,15,16,18,[21][22][23] , where interactions and replacements are constrained to two different networks. A multiplex network is a network consisting of multiple layers, where nodes are common to all layers while edges are connected in different ways in each layer.…”