Graph Embedding is a recently introduced technique whose goal is to transform graph data into vector data by keeping some aspects of the graph structure. Because it allows to apply all the range of data mining and machine learning techniques -that require vectors as input- to graph data, it has the potential to be a game changer in the related fields of graph mining/network analysis. Thus, effective graph analytics, based on embedding representations, can benefit a lot of applications such as node classification, node clustering, node retrieval/ recommendation, link prediction, visualization to cite but a few examples.
The aim of this workshop is to group together researchers workings on all topics concerned by the graph embedding/Representation Learning aspect, from the proposition of new methods, to application to relevant problems. Theoretical, methodological and experimental contributions are also welcomed.