Graph-based methods have been demonstrated as one of the most effective approaches for
semi-supervised learning, as they can exploit the connectivity patterns between labeled and …

Personalized recommendation is ubiquitous, playing an important role in many online
services. Substantial research has been dedicated to learning vector representations of …

Despite the exploding interest in graph neural networks there has been little effort to verify
and improve their robustness. This is even more alarming given recent findings showing that …

We consider a family of problems that are concerned about making predictions for the
majority of unlabeled, graph-structured data samples based on a small proportion of labeled …

Deep learning models have achieved state-of-the-art performance in classifying nodes in
graph-structured data. However, recent work has shown that these models are vulnerable to …

Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task
of classification in various applications. However, NNs have not considered any types of …

Graph convolutional neural networks (GCNN) have numerous applications in different graph
based learning tasks. Although the techniques obtain impressive results, they often fall short …

In this work, we propose a probabilistic framework for relational data modelling and latent
structure exploring. Given the possible feature information for the nodes in a network, our …

Graph convolutional neural networks (GCNN) have been successfully applied to many
different graph based learning tasks including node and graph classification, matrix …

We introduce Graph Neural Processes (GNP), inspired by the recent work in conditional and
latent neural processes. A Graph Neural Process is defined as a Conditional Neural Process …