Multivariate Network Embedding for Visual Analytics

This doctoral project aims to explore the use of embedding technologies for visual and computational analytics on large multivariate networks. We focus on new ways to combine already existing embedding types so that synergetic effects can be obtained.

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 target 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 computational analytics.) 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 the strategy would be to embed each property by itself and then find ways to combine these sets of embeddings to obtain synergetic effects.