Artículo Científico presentado por
Miembro del Programa de Honores. Licenciatura en Ciencia de Datos. Departamento de Actuaría, Física y Matemáticas. Escuela de Ciencias, Universidad de las Américas Puebla.
Jurado Calificador
Director: Dr. Gerardo Arizmendi Echegaray
Presidente: Dr. Hugo Villanueva
Méndez
Secretario: Dr. Freddy Palma Mancilla
Cholula, Puebla, México a 19 de noviembre de 2024.
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.
Keywords: Probability, Machine Learnings, Graphs, Networks, Node Classification.
Chapter 1. Introduction
Chapter 2. Preliminaries
Chapter 3. Model
Chapter 4. Parameter estimation
Chapter 5. Node Classification
Chapter 6. Math Genealogy Project
Chapter 7. Ogbn-arxiv dataset
Chapter 8. Conclusion
References
Huerta Ocaña, D. 2024. A Probabilistic Model for Node Classification in Directed Graphs. Artículo Científico Licenciatura. Ciencia de Datos. Departamento de Actuaría, Física y Matemáticas, Escuela de Ciencias, Universidad de las Américas Puebla. Noviembre. Derechos Reservados © 2024.