In this paper, we address the problem of dynamic network embedding, that is, representing the nodes of a dynamic network as evolving vectors within a low-dimensional space. While the field of single-graph embedding is wide and established, the field of dynamic graph embedding is comparatively in its infancy. In this paper, we propose that we can take a wide class of established single-graph embedding methods and use them to produce interpretable and powerful dynamic graph embeddings by simply applying them to the dilated unfolded matrix. We provide a theoretical guarantee that, regardless of embedding dimension, these unfolded methods will produce stable embeddings over time and space, meaning that nodes with identical latent behaviour will be exchangeable, regardless of their position in time or space.
My work focuses on dynamic network embedding. Dynamic networks encode both spatial and temporal relations between nodes, which are desirable for many applications from cyber security to neuroscience. By representing the nodes of the network as a vector space, it allows us to detect spatial and temporal patterns through clustering. Methods that I have developed during my PhD have been used by Microsoft and LV, and I have presented these methods at the Institute of Mathematics Big Data Conference and the COMPASS conference in 2022. I am interested in progressing dynamic embedding methods and applying them to a diverse range of problems.
A Simple but Powerful Framework for Dynamic Graph Embedding
Award-Winning Data Visualisation
Runner-up in the Jean Golding Institute's Beauty of Data competition. This visualisation was presented at the Bristol Data and AI Showcase 2022. This visualisation displays an embedding of a friendship network between countries on the world stage based on their alliances.
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