Using a theoretical simulation framework to analyse and develop predictive machine learning methods on networks

本研究项目建立在已经开发的网络预测评级验证的理论模拟框架的基础上。通过模拟框架，可以生成人工数据，复制完整的预测过程，包括网络生成、预测评级的创建和从这些评级得出百分比预测。人工数据的优势在于，与真实数据相比，所有固有的过程都可以被有意地控制和改变。这使得分析网络结构对预测质量的准确影响成为可能，并且还使得检验模型的精确度测量和盈利测量得以改进。虽然经典的统计模型已经在上一个项目中得到了成功的验证，但本研究项目的重点是预测机器学习方法在网络上的理论验证和进一步发展。考虑到来自足球和网球的复杂数据集，体育部门的数据是一个应用示例。关于ML模型，该项目涉及监督学习的方法，这些方法将被指定、实施、纳入现有的模拟框架，并在第一个工作包中进行功能测试。将考虑四类不同的模型，两类基于随机森林和图神经网络的纯ML模型类，以及两类结合基于ML的方法和经典统计方法的混合模型类。在第二个工作包中，使用来自仿真框架的人工数据来验证基于ML的模型。特别是，我们的目标是确定不同的网络和数据结构如何影响模型的预测质量。这包括识别ML模型、混合模型或经典模型优于其他模型的情况。这个研究问题的部分原因是，在预测过程中(例如在经济学中)，最大似然模型的表现还没有超过传统方法。输入数据的处理和模型输出的验证与ML模型的可解释性问题密切相关。通过分析模型质量，找出模型的优点和缺点，我们打算对基于ML的模型的潜在进一步发展得出结论，这些模型可以在工作包3中实现和重新验证。输入数据的操作和模型输出的验证是在最后的工作包中，基于ML的模型被应用于实际数据集，以确保理论见解可以转移到现实世界的应用中。同样，它的目的是通过分析模型的优点和缺点来确定进一步改进模型的潜力。

The present research project builds upon a theoretical simulation framework for the validation of predictive ratings on networks, which has been developed. By means of the simulation framework, artificial data can be generated that replicates a full predictive process, involving network generation, creation of predictive ratings and derivation of percentage forecasts from these ratings. The advantage of artificial data is that, in contrast to real data, all inherent processes can be deliberately controlled and varied. This makes it possible to analyze the exact influence of the network structure on the predictive quality and also enables improved accuracy measures and profitability measures for examining the models. While classical statistical models were already successfully validated in the previous project, the present research project focuses on the theoretical validation and further development of predictive machine learning (hereafter abbreviated as ML) methods on networks. Data from the sports sector serve as an application example, considering complex data sets from football and tennis. With regard to ML models, the project addresses methods of supervised learning, which will be specified, implemented, integrated into the existing simulation framework and tested for functionality in the first work package. Four different classes of models will be considered, two pure ML model classes based on Random Forest and Graph Neural Networks as well as two hybrid model classes combining ML-based methods with classical statistical methods. In the second work package, the ML-based models are validated using artificial data from the simulation framework. In particular, we aim to determine how the predictive quality of the models is influenced by varying network and data structures. This includes the identification of situations in which ML, hybrid or classical models are superior to the other models. This research question is partly inspired by the fact that in predictive processes (e.g. in economics) ML models do not yet outperform traditional methods. The manipulation of input data and validation of model outputs is closely related to the question of interpretability for ML models. By analyzing the model quality and identifying strengths and weaknesses of the models, we intend to draw conclusions about potential further development of ML-based models, which can be implemented and revalidated within work package three. The manipulation of input data and validation of model outputs is In the last work package, the ML-based models are applied to real datasets in order to ensure the transferability of theoretical insights to real-world applications. Again, it is intended to identify potential for further model improvement by analyzing strength and weaknesses of the models.