MS22: Adaptation and Propagation Processes on Networks - From Neural Networks to Epidemic Spread and Market Ecosystems
The aim of this minisymposium is to present results of modeling propagation and adaptation processes ranging from neural networks to epidemic spread and market ecosystems. By propagation processes we refer to those dynamics that change the states of the nodes depending on that of its neighbors and by adaptation processes we refer to the dynamics that change the network structure in response to its current state. To investigate such processes different techniques are employed, ranging from individual based stochastic simulation, through ODE and PDE modeling via adaptive network Markov chains to data analysis of networked systems.
In this progression from abstract to applied, we will first explore the relation between the network structure and the qualitative behavior of the solutions of the corresponding differential equations in neural networks. Individual based epidemic propagation models are presented on networks with special structure.
Then we explore adaptation processes using Markov chain network models of stylized ecosystems and conclude with the analysis of real world adaptive market ecosystems. These topics are especially relevant for mathematical consulting tasks by helping clients to model and understand their adaptation and propagation processes in a complex network environment.
Thursday, April 15, 14:30-16:10
- 14:30 Leonhard Horstmeyer, Adaptive Networks and Collapses in Ecosystems and Epidemics
- 14:55 Ágnes Backhausz, Edit Bognár, Péter L. Simon, Epidemic spread in random-graph based household models
- 15:20 Nga Nguyen, Network as Social Norms - Sustainable Coffee on Twitter
- 15:45 Peter L. Simon, The effect of inhibitory neurons to bifurcations in neural network models