Graph structured data are ubiquitous data structures, used to model relationships between entities. Graphs have become an important foundation to represent interactions between users in social networks, items in recommender systems, and interactions between drugs in bioinformatics. The main research problems in these areas include node clustering, node classification and link prediction. Especially the link prediction task is in bioinformatics of special interest toward the identification and development of new uses of existing or abandoned drugs since drug development is currently very time consuming and expensive. In the context of knowledge graphs, link prediction is also of special interest to automatically complete missing information to derive further knowledge. Likewise, node classification is an important research focus in the context of knowledge graphs, e.g. to automatically classify new entities according to their class affiliation and to complete missing class affiliation for existing entities.
In recent years, network embeddings are often trained for encoding the entities of graph structured data into a low-dimensional space whilst preserving the graph structure. ... mehr