提出可解释的有向图节点分类概率模型，性能媲美甚至超越主流方法。
A Probabilistic Model for Node Classification in Directed Graphs
- 基于概率生成机制，利用节点属性和标签建模有向图结构
- 在两个数据集上表现优于或持平当前最优方法，包括新构建的数学家谱系数据集
- 适合需要可解释性、对模型透明度要求高的场景
本文提出一种针对带属性和标签的有向图的生成式概率模型，可用于未见节点的标签预测，支持最大似然或最大后验估计。该模型预测结果高度可解释，与图神经网络等常见方法形成对比。我们在两个数据集上进行了实验，结果显示其预测性能与当前最先进方法相当，甚至更优。其中一个数据集源自数学家谱系项目（Math Genealogy Project），此前未被用于此类任务，因此我们在此基础上评估了多种分类算法，为该数据集提供了基准参考。
原文摘要 · Abstract (English)
In this work, we present a probabilistic model for directed graphs where nodes have attributes and labels. This model serves as a generative classifier capable of predicting the labels of unseen nodes using either maximum likelihood or maximum a posteriori estimations. The predictions made by this model are highly interpretable, contrasting with some common methods for node classification, such as graph neural networks. We applied the model to two datasets, demonstrating predictive performance that is competitive with, and even superior to, state-of-the-art methods. One of the datasets considered is adapted from the Math Genealogy Project, which has not previously been utilized for this purpose. Consequently, we evaluated several classification algorithms on this dataset to compare the performance of our model and provide benchmarks for this new resource.
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