Network Representation Learning

Learning deep representations from raw input data has revolutionised the field of Machine Learning in the past few years. While deep representation learning has been extremely successful in computer vision and natural language processing, there have been very few studies involving representation learning on network data (for example, social network data). We propose to develop a theory of network representation learning, along the lines of the very popular and successful methods for text representation. The theory should be able to represent network features at different levels of granularity (node level, edge level, path level, etc.), as well as handle different kinds of networks (directed, weighted, temporal, multiplex, etc.).