The leading approaches in Machine Learning are notoriously data-hungry. Unfortunately,
many application domains do not have access to big data because acquiring data involves a …

Semi-supervised learning is the branch of machine learning concerned with using labelled
as well as unlabelled data to perform certain learning tasks. Conceptually situated between …

Many interesting problems in machine learning are being revisited with new deep learning
tools. For graph-based semi-supervised learning, a recent important development is graph …

Few-shot learning is challenging for learning algorithms that learn each task in isolation and
from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that …

Abstract Graph Convolutional Networks (GCNs) play a crucial role in graph learning tasks,
however, learning graph embedding with few supervised signals is still a difficult problem. In …

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 …

Recently, neural network based approaches have achieved significant progress for solving
large, complex, graph-structured problems. Nevertheless, the advantages of multi-scale …

We introduce propagation kernels, a general graph-kernel framework for efficiently
measuring the similarity of structured data. Propagation kernels are based on monitoring …

Graph convolutional networks (GCNs) have shown promising results in processing graph
data by extracting structure-aware features. This gave rise to extensive work in geometric …

Graph neural networks (GNNs) are among the most powerful tools in deep learning. They
routinely solve complex problems on unstructured networks, such as node classification …