13 citations · 29 across the 5 of their papers we have counts for
5 papers
Node Copying for Protection Against Graph Neural Network Topology Attacks
Florence Regol, Soumyasundar Pal, Mark Coates
Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigati…
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation
Florence Regol, Soumyasundar Pal, Yingxue Zhang +1
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can impro…
Non-Parametric Graph Learning for Bayesian Graph Neural Networks
Soumyasundar Pal, Saber Malekmohammadi, Florence Regol +3
Graphs are ubiquitous in modelling relational structures. Recent endeavours in machine learning for graph-structured data have led to many architectures and learning algorithms. Ho…
Bayesian Graph Convolutional Neural Networks using Node Copying
Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have numerous applications in different graph based learning tasks. Although the techniques obtain impressive results, they often fall sh…
Bayesian Graph Convolutional Neural Networks Using Non-Parametric Graph Learning
Soumyasundar Pal, Florence Regol, Mark Coates
Graph convolutional neural networks (GCNN) have been successfully applied to many different graph based learning tasks including node and graph classification, matrix completion, a…