Graphs and more Complex structures for Learning and Reasoning (GCLR) workshop was held at AAAI 2021. For more details about the workshop, please visit website: https://sites.google.com/view/gclr2021/.
Speaker's Bio: Prof. Ginestra Bianconi is Professor of Applied Mathematics in the School of Mathematical Sciences of Queen Mary University of London and she is Alan Turing Fellow at the Alan Turing Institute. Her research activity on Statistical Mechanics and Network Science includes Network Theory and its interdisciplinary applications. In this talk, Ginestra will talk about Information theory of networks and explain the emergence of heterogeneity in complex networks.
Title of the talk: Information theory of networks
Abstract: Information theory is one of the most fundamental theoretical frameworks of network science and machine learning. However, the current information theory frameworks for understanding networks, based on maximum entropy network ensembles, are not able to explain the emergence of heterogeneity in complex networks. Here, we fill this gap of knowledge by developing a information theoretical framework for networks based on finding a trade-off between the information content of a compressed representation of the ensemble and the information content of the actual network ensemble.