Multiple Embeddings for Multivariate Network Analysis

The visualization and visual analytics of large multivariate networks (MVN) continues to be a great challenge and will probably remain so for a foreseeable future. The field of Multivariate Network Embedding seeks to meet this challenge by providing MVN-specific embedding technologies that targets different properties such as network topology or attribute values for nodes or links. Embeddings are relatively low-dimensional vector representations of the embedded items and they are well suited for similarity calculations. Although many steps forward have been taken, the goal of efficiently embedding all aspects of a MVN remains distant. As a possible way forward we suggest a new angle of approach where, instead of trying to fit all aspects of a MVN into one embedding, the strategy would be to embed each property by itself and then find ways to combine these sets of embeddings