Recently, there has been an increasing interest in (supervised) learning with graph data,
especially using graph neural networks. However, the development of meaningful …

Experimental reproducibility and replicability are critical topics in machine learning. Authors
have often raised concerns about their lack in scientific publications to improve the quality of …

In the last decade or so, we have witnessed deep learning reinvigorating the machine
learning field. It has solved many problems in the domains of computer vision, speech …

Graph Neural Networks (GNNs) are an effective framework for representation learning of
graphs. GNNs follow a neighborhood aggregation scheme, where the representation vector …

Graph Neural Networks (GNNs) are the predominant technique for learning over graphs.
However, there is relatively little understanding of why GNNs are successful in practice and …

Graph Neural Nets (GNNs) have received increasing attentions, partially due to their
superior performance in many node and graph classification tasks. However, there is a lack …

Semi-supervised node classification in graphs is a fundamental problem in graph mining,
and the recently proposed graph neural networks (GNNs) have achieved unparalleled …

Although there exist several libraries for deep learning on graphs, they are aiming at
implementing basic operations for graph deep learning. In the research community …

Graph neural networks (GNNs) have achieved state-of-the-art performance on graph
classification tasks. Existing work usually feeds graphs to GNNs in random order for training …

Machine learning over graphs have been emerging as powerful learning tools for graph
data. However, it is challenging for industrial communities to leverage the techniques, such …