In many industrial applications, it is common that the graph embeddings generated from training GNNs are used in an ensemble model where the embeddings are combined with other tabular features (e.g., original node or edge features) in a downstream ML task. The tabular features may even arise naturally if, e.g., one tries to build a graph such that some of the node or edge features are stored in a tabular format. Here we address the problem of explaining the output of such ensemble models for which the input features consist of learned neural graph embeddings combined with additional tabular features. We propose MBExplainer, a model-agnostic explanation approach for downstream models with augmented graph embeddings. MBExplainer returns a human-legible triple as an explanation for an instance prediction of the whole pipeline consisting of three components: a subgraph with the highest importance, the topmost important nodal features, and the topmost important augmented downstream features. A game-theoretic formulation is used to take the contributions of each component and their interactions into account by assigning three Shapley values corresponding to their own specific games. Finding the explanation requires an efficient search through the corresponding local search spaces corresponding to each component. MBExplainer applies a novel multilevel search algorithm that enables simultaneous pruning of local search spaces in a computationally tractable way. In particular, three interweaved Monte Carlo Tree Search are utilized to iteratively prune the local search spaces. MBExplainer also includes a global search algorithm that uses contextual bandits to efficiently allocate pruning budget among the local search spaces. We show the effectiveness of MBExplainer by presenting a set of comprehensive numerical examples on multiple public graph datasets for both node and graph classification tasks.

翻译：在许多工业应用中，训练图神经网络生成的图嵌入通常被用于集成模型中，其中嵌入在下游机器学习任务中与其他表格特征（例如原始节点或边特征）相结合。表格特征甚至可能自然产生，例如，当尝试构建图时，部分节点或边特征以表格格式存储。本文致力于解释此类集成模型的输出，其输入特征由学习得到的神经图嵌入与附加表格特征组合而成。我们提出MBExplainer，一种面向增强图嵌入下游模型的模型无关解释方法。MBExplainer返回人类可读的三元组作为整个预测流程的实例解释，包含三个组成部分：最高重要性的子图、最重要的节点特征以及最重要的增强下游特征。通过博弈论框架，我们为每个组件及其相互作用分配三个对应的沙普利值，以考量它们的具体博弈贡献。寻找解释需要在各组件对应的局部搜索空间中进行高效搜索。MBExplainer采用一种新颖的多级搜索算法，能够以计算可处理的方式同时剪枝局部搜索空间。具体而言，该方法利用三个交织的蒙特卡洛树搜索迭代剪枝局部搜索空间。MBExplainer还包含一种全局搜索算法，通过上下文赌博机高效分配局部搜索空间之间的剪枝预算。我们通过在多个公共图数据集上针对节点分类和图分类任务进行综合数值实验，展示了MBExplainer的有效性。