Model Selection of Graph Signage Models Using Maximum Likelihood (Student Abstract)

Authors

  • Angelina Brilliantova Rochester Institute of Technology
  • Ivona Bezáková Rochester Institute of Technology

DOI:

https://doi.org/10.1609/aaai.v37i13.26944

Keywords:

Signed Networks, Gene Regulatory Networks, Maximum-likelihood, Markov Chains, Model Fitting

Abstract

Complex systems across various domains can be naturally modeled as signed networks with positive and negative edges. In this work, we design a new class of signage models and show how to select the model parameters that best fit real-world datasets using maximum likelihood.
AAAI-23 Proceedings Cover

Downloads

Published

2023-09-06

How to Cite

Brilliantova, A., & Bezáková, I. (2023). Model Selection of Graph Signage Models Using Maximum Likelihood (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 16168-16169. https://doi.org/10.1609/aaai.v37i13.26944

Issue

Vol. 37 No. 13: AAAI-23 Special Programs, IAAI-23, EAAI-23, Student Papers and Demonstrations

Section

AAAI Student Abstract and Poster Program