III: Small: Transfer Learning Within and Across Networks for Collective Classification

关系机器学习方法可以显著提高从社交网络到物理和生物网络的一系列网络域的模型的预测准确性。该方法自动学习网络相关模式（例如，在生物网络中，一对相互作用的蛋白质比两个随机选择的蛋白质更可能具有相同的功能），然后在集体推理过程中使用它们来在整个网络中传播预测。这些关系方法的主要假设是，从一个网络估计的模型参数适用于从同一分布绘制的其他网络。然而，很少有人研究这种假设的影响，特别是网络结构的变化如何影响关系模型和集体推理的性能。该项目旨在研究这一问题，以超越网络/数据来自相同底层分布的隐含假设。该研究将建立一个跨异构网络结构学习的形式化框架，并描述网络结构对属性相关模型的影响。这些发现将加深我们对关系模型性能如何/何时在网络数据集上泛化的理解，这项工作将开发新的方法来提高泛化能力。 更具体地说，在这个项目中，PI进行了关键观察，即模板化的图形模型经常用于网络分类方法。这些模型由小的（即，本地）模型模板，这些模板在异构网络上"铺开"，以动态构建具有可变结构的更大模型，用于估计和推断。由于推出过程，学习模型的泛化能力将取决于用于学习和预测的网络之间的相似性。在这个项目中，PI将通过将关系学习和集体推理形式化为“迁移学习”问题来更深入地研究这个问题，目标是从一个领域学习模型，并成功地将其应用到不同的领域。该研究将调查如何在网络中最好地传递学到的知识（即，从网络的一个标记部分到另一个标记部分），以及跨网络（即，从群体中的一个网络到另一个网络）。该项目将开发严格的统计方法和先进的计算算法，通过四个具体目标来回答这个问题：（目标1）评估网络内和网络间可转移性的正式基础;（目标2）用于实证调查的属性网络生成模型;（目标3）非平稳数据的网络内转移方法;（目标4）网络内转移方法。以及（Aim4）使用模板匹配和全局平滑的跨网络传输方法。

Relational machine learning methods can significantly improve the predictive accuracy of models for a range of network domains, from social networks to physical and biological networks. The methods automatically learn network correlation patterns (e.g., in biological networks a pair of interacting proteins are more likely to have the same function than two randomly selected proteins) from observed data and then use them in a collective inference process to propagate predictions throughout the network. The primary assumption in these relational methods is that model parameters estimated from one network are applicable to other networks drawn from the same distribution. However, there has been little work studying the impact of this assumption, and in particular how variability in network structure affects the performance of relational models and collective inference. This project aims to investigate this issue in order to move beyond the implicit assumption that the networks/data are drawn from the same underlying distribution. The research will establish a formal framework for learning across heterogeneous network structures and characterize the impact of network structure on models of attribute correlation. The findings will deepen our understanding of how/when relational model performance generalizes across network datasets and the work will develop new methods to improve generalization. More specifically, in this project the PI makes the key observation that templated graphical models are often used in network classification methods. These models are composed of small (i.e., local) model templates that are ''rolled out'' over a heterogeneous network to dynamically construct a larger model with variable structure for estimation and inference. Due to the roll out process, the generalizability of a learned model will depend on the similarity between the networks used for learning and prediction. In this project, the PI will study this issue in greater depth by formalizing relational learning and collective inference as a ''transfer learning'' problem, with the goal of learning a model from one domain and successfully applying it to a different domain. The research will investigate how to best transfer learned knowledge within networks (i.e., from one labeled part of a network to another), and across networks (i.e., from one network in a population to another). The project will develop rigorous statistical methods and advanced computational algorithms to answer this question via four specific aims: (Aim1) formal foundation for assessing transferability within and across networks; (Aim2) generative models of attributed networks for empirical investigation; (Aim3), within-network transfer methods for non-stationary data; and (Aim4) across-network transfer methods using template matching and global smoothing.