Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graphs naturally appear in numerous application domains, ranging from social analysis,
bioinformatics to computer vision. The unique capability of graphs enables capturing the …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

Graph neural networks have shown significant success in the field of graph representation
learning. Graph convolutions perform neighborhood aggregation and represent one of the …

Recent works on representation learning for graph structured data predominantly focus on
learning distributed representations of graph substructures such as nodes and subgraphs …

Graph-structured data naturally appear in numerous application domains, ranging from
social analysis, bioinformatics to computer vision. The unique capability of graphs enables …

The Transformer architecture has gained growing attention in graph representation learning
recently, as it naturally overcomes several limitations of graph neural networks (GNNs) by …

The dominant graph neural networks (GNNs) over-rely on the graph links, several serious
performance problems with which have been witnessed already, eg, suspended animation …

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 …