Representations for temporal networks: a talk by Laetitia Gauvin at Workshop on Higher-order Interaction Networks in Oxford

Many complex systems in physics, biology, neuroscience, engineering, and social science can be modeled as networks of interacting nodes. In many cases, each node is a dynamical system which evolves under the influence of the other nodes it is connected to in the network. The Workshop on Higher-order Interaction Networks: Dynamics, Structure, Data aims at bringing together researchers from different communities with distinct perspectives on network dynamics — from network science, dynamical systems, and data science/machine learning — to develop novel approaches to understand networked systems.

The workshop is being held at the Mathematical Institute of the University of Oxford from 9th to 11th September, and among the invited speakers is ISI Foundation Research Leader Laetitia Gauvin. Monday 9th at 15.30, Laetitia Gauvin will give a talk about “Representations for temporal networks”. Going beyond exact representations of temporal networks, there has been a recent surge in the development of methods to create embedding for networks that preserve important properties of the original structure, while representing it in a lower dimensional space. Along these lines, Laetitia Gauvin will present some representation learning approaches to estimate epidemic sizes.

“Representations for temporal networks”, Laetitia Gauvin, Workshop on Higher-order Interaction Networks: Dynamics, Structure, Data, September 9th, University of Oxford (UK). Link