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GraPhTra focuses on understanding phase transitions in random graphs, with applications in neuromorphic computing and epidemiology. The project introduces a new model for sampling graphs and explores the conditions for estimating percolation thresholds, contributing to the theoretical framework of graph theory and its…
GraPhTra aims to advance the rigorous theoretical understanding of phase transitions in random graphs and their universal properties, driven by questions in neuromorphic computing, physics, and epidemiology.
We will work on three work packages.-We introduce a new model for random graphs, that are sampled with local energy considerations, inspired by statistical physics and motivated by material science for neuromorphic computing.
We use a Gibbs measure to sample a spanning tree of a graph, and b…
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