241 citations · 715 across the 25 of their papers we have counts for
10 papers · 1 filter
Solving the Kidney-Exchange Problem via Graph Neural Networks with No Supervision
Pedro Foletto Pimenta, Pedro H. C. Avelar, Luis C. Lamb
This paper introduces a new learning-based approach for approximately solving the Kidney-Exchange Problem (KEP), an NP-hard problem on graphs. The problem consists of, given a pool…
Graph-based Neural Modules to Inspect Attention-based Architectures: A Position Paper
Breno W. Carvalho, Artur D'Avilla Garcez, Luis C. Lamb
Encoder-decoder architectures are prominent building blocks of state-of-the-art solutions for tasks across multiple fields where deep learning (DL) or foundation models play a key…
Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
Henrique Lemos, Pedro Avelar, Marcelo Prates +2
The recent developments and growing interest in neural-symbolic models has shown that hybrid approaches can offer richer models for Artificial Intelligence. The integration of effe…
Superpixel Image Classification with Graph Attention Networks
Pedro H. C. Avelar, Anderson R. Tavares, Thiago L. T. da Silveira +2
This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regi…
Discrete and Continuous Deep Residual Learning Over Graphs
Pedro H. C. Avelar, Anderson R. Tavares, Marco Gori +1
In this paper we propose the use of continuous residual modules for graph kernels in Graph Neural Networks. We show how both discrete and continuous residual layers allow for more…
Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems
Henrique Lemos, Marcelo Prates, Pedro Avelar +1
Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learn…
Typed Graph Networks
Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos +2
Recently, the deep learning community has given growing attention to neural architectures engineered to learn problems in relational domains. Convolutional Neural Networks employ p…
Machine Learning in Network Centrality Measures: Tutorial and Outlook
Felipe Grando, Lisando Z. Granville, Luis C. Lamb
Complex networks are ubiquitous to several Computer Science domains. Centrality measures are an important analysis mechanism to uncover vital elements of complex networks. However,…
Neural Networks Models for Analyzing Magic: the Gathering Cards
Felipe Zilio, Marcelo Prates, Luis Lamb
Historically, games of all kinds have often been the subject of study in scientific works of Computer Science, including the field of machine learning. By using machine learning te…
Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP
Marcelo O. R. Prates, Pedro H. C. Avelar, Henrique Lemos +2
Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial domains. GNNs employ trainable modules which can be assembled in diffe…