Abstract Machine learning (ML) is continuously unleashing its power in a wide range of
applications. It has been pushed to the forefront in recent years partly owing to the advent of …

Sequencing technologies are evolving at a rapid pace, enabling the generation of massive
amounts of data in multiple dimensions (eg, genomics, epigenomics, transcriptomic …

Node classification is an important problem in relational machine learning. However, in
scenarios where graph edges represent interactions among the entities (eg, over time), the …

This paper studies structured node classification on graphs, where the predictions should
consider dependencies between the node labels. In particular, we focus on solving the …

Revised: 22/Dec/2017, Accepted: 20/Jan/2018, Published: 31/Jan/2018 Abstract-Big data is
a wonderful supply of information and knowledge from the systems to other end-users …

Collective Inference (CI) is a procedure designed to boost weak relational classifiers,
specially for node classification tasks. Graph Neural Networks (GNNs) are strong classifiers …

Collective inference is widely used to improve classification in network datasets. However,
despite recent advances in deep learning and the successes of recurrent neural networks …

We consider the problem of node classification in heterogeneous graphs, where both nodes
and relations may be of different types, and different sets of categories are associated to …

Irrigation decision-making has evolved from solely depending on farmers' decisions taken
based on the visual analysis of field conditions to making decisions based on crop water …

We address the task of node classification in heterogeneous networks, where the nodes are
of different types, each type having its own set of labels, and the relations between nodes …