A Probabilistic Model for Node Classification in Directed Graphs
This work addresses node classification for researchers in graph analysis, but it is incremental as it builds on existing probabilistic approaches.
The authors tackled node classification in directed graphs by developing a probabilistic model that predicts labels with interpretable results, achieving competitive or superior performance to state-of-the-art methods on two datasets, including a new one from the Math Genealogy Project.
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.