Integrative Learning of Dynamically Evolving Multiplex Graphs and Nodal Attributes Using Neural Network Gaussian Processes with an Application to Dynamic Terrorism Graphs
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利用神经网络高斯过程联合学习动态多层图与节点属性
- 研究问题
- 在多层网络部分关系不可观测时，如何联合学习网络连接与节点属性的动态共同演化，并对缺失关系预测表达不确定性？
- 方法与思路
- 作者构建时变随机潜在因子模型，以共享因子连接各图层和节点属性，并用无限宽深度神经网络协方差的高斯过程建模。
- 材料陈述的贡献
- 摘要提出动态联合学习器，称模拟表现优于比较方法，并用于恐怖组织动态关系、组织属性及敌友聚类的带不确定性预测。
- 阅读关注点
- 应核对各层与属性共享信息的机制、潜在因子的可识别性、部分观测的建模方式，以及预测区间与聚类结果的验证。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未经核实；摘要未说明缺失机制或比较细节，共同演化和预测结果不能直接解释为因果关系。
原文摘要
Exploring the dynamic co-evolution of multiplex graphs and nodal attributes is a compelling question in criminal and terrorism networks. This article is motivated by the study of dynamically evolving interactions among prominent terrorist organizations, considering various organizational attributes like size, ideology, leadership, and operational capacity. Statistically principled integration of multiplex graphs with nodal attributes is significantly challenging due to the need to leverage shared information within and across layers, account for uncertainty in predicting unobserved links, and capture temporal evolution of node attributes. These difficulties increase when layers are partially observed, as in terrorism networks where connections are deliberately hidden to obscure key relationships. To address these challenges, we present a principled methodological framework to integrate the multiplex graph layers and nodal attributes. The approach employs time-varying stochastic latent factor models, leveraging shared latent factors to capture graph structure and its co-evolution with node attributes. Latent factors are modeled using Gaussian processes with an infinitely wide deep neural network-based covariance function, termed neural network Gaussian processes (NN-GP). The NN-GP framework on latent factors exploits the predictive power of Bayesian deep neural network architecture while propagating uncertainty for reliability. Simulation studies highlight superior performance of the proposed approach in achieving inferential objectives. The approach, termed as dynamic joint learner, enables predictive inference (with uncertainty) of diverse unobserved dynamic relationships among prominent terrorist organizations and their organization-specific attributes, as well as clustering behavior in terms of friend-and-foe relationships, which could be informative in counter-terrorism research.