5 papers
HCPN-GCN: Scaling Hierarchical Prototype Networks with Cone Geometry for Continual Graph Learning
Sammuel R. Silva, Vander L. S. Freitas, Gladston Moreira +2
Continual Graph Learning (CGL) aims to incrementally learn from graph-structured data while preserving knowledge acquired from previous tasks. A major challenge in this setting is…
Benford's Law as a Distributional Prior for Post-Training Quantization of Large Language Models
Arthur Negrão, Pedro Silva, Vander L. S. Freitas +2
Post-training quantization (PTQ) is a practical way to reduce the memory footprint of large language models, but low-bit quantization is sensitive to mismatches between the quantiz…
A High-Performance Evolutionary Multiobjective Community Detection Algorithm
Guilherme O. Santos, Lucas S. Vieira, Giulio Rossetti +2
Community detection in complex networks is fundamental across social, biological, and technological domains. While traditional single-objective methods like Louvain and Leiden are…
Leveraging graph neural networks and mobility data for COVID-19 forecasting
Fernando H. O. Duarte, Gladston J. P. Moreira, Eduardo J. S. Luz +2
The COVID-19 pandemic has claimed millions of lives, spurring the development of diverse forecasting models. In this context, the true utility of complex spatio-temporal architectu…
Leveraging Visibility Graphs for Enhanced Arrhythmia Classification with Graph Convolutional Networks
Rafael F. Oliveira, Gladston J. P. Moreira, Vander L. S. Freitas +1
Arrhythmias, detectable through electrocardiograms (ECGs), pose significant health risks, underscoring the need for accurate and efficient automated detection techniques. While rec…