The notion of affinity among countries is central in studies of international relations: it plays an important role in research as scholars use measures of affinity to study conflict and cooperation in a variety of contexts. To more effectively measure affinity, I argue that it is necessary to utilize multidimensional data and take into account the network context of international relations. In this paper, I develop the deep affinity concept and introduce a new algorithm, the three-step graphical LASSO (GLASSO), to infer and recover latent affinity networks. This technique leverages the abundance of monadic and dyadic state-level data to identify the presence or absence of affinity links between pairs of countries. Directly incorporating network effects and using a variety of multidimensional data inputs, I used the three-step GLASSO to estimate latent affinity links among countries. With these data, I examined the implications of affinity for international conflict and foreign direct investment, and found that the measure of affinity generated with the three-step GLASSO outperformed alternative affinity measures and was associated with decreased conflict and increased economic interaction.

中文翻译：

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朋友和合作伙伴：使用图形 LASSO 估计潜在亲和网络


国家间亲和力的概念是国际关系研究的核心：它在研究中发挥着重要作用，因为学者们使用亲和力来研究各种背景下的冲突与合作。为了更有效地衡量亲和力，我认为有必要利用多维数据并考虑国际关系的网络背景。在本文中，我开发了深度亲和力概念，并引入了一种新算法，即三步图形 LASSO （GLASSO），用于推断和恢复潜在的亲和网络。该技术利用丰富的单子和二元国家级数据来识别国家对之间是否存在亲和力。我直接结合网络效应并使用各种多维数据输入，使用三步 GLASSO 来估计国家之间的潜在亲和力链接。利用这些数据，我研究了亲和力对国际冲突和外国直接投资的影响，发现用三步 GLASSO 生成的亲和力度量优于其他亲和力度量，并且与减少冲突和增加经济互动相关。