On this website you can find a list of animations developed for the Network Pages. These animations were developed by Robert Fitzner and Martijn Gösgens
from the Eindhoven University of Technology. Feel free to use them for educational and scientific purposes.
We would like to ask you though to mention the Network Pages and link to the website whenever you use an animation. If you have suggestions or wishes for specific animations or
simulations you can contact us at editor@networkpages.nl. Thanks in advance!

This animation shows how Dijkstra's algorithm works. This algorithm is used to compute the shortest paths between locations. As an example we use the streets of the Dutch town of Brielle. You can also read this article on Dijkstra's algorithm.

This animation shows in steps how Dijkstra's algorithm works, on a simple graph. You can also read this article on Dijkstra's algorithm.

An example of machine learning using a simple algorithm. We implemented the mechanics of jumping, better say falling,
and let the computer try to land on the pillow until it succeeds. Once it succeeded in landing, the program stops.

- Simple trial and error to jump on top.
- Trial and error, but by using the best previous attempt as initial point.
- The program looks for solutions that require a small initial speed.

A spectral partitioning algorithm that is used to detect bottlenecks in networks.
Used in this article written by Viresh Patel.

Various versions of an Erdős-Rényi Graph with the parameters given by the user.

Generates a graph from the Configuration model with the parameters given by the user. We also have an extension where we show a random walk on the resulting graph. Once the random walk starts, the color of the nodes indicates the local time. The aim it show mixing of the random walk.

This *Percolation game* was made for a study day of the Dutch Association of Mathematics Teachers.
Detailed instructions can be found
here.

A variant of the classic *Minesweeper* game where the grid is replaced by an arbitrary network. The game is described in this article
written by Martijn Gösgens.

A simulation of a network with a community structure. In this simulation we generate a random network with communities and we show how an infection can spread on the nodes of the network.

Simulation of the two-community Kuramoto model for synchronization of the body clock. Used in this article by Janusz Meylahn.

Simulation percolation on 3D grid. Used in this article by Nicos Starreveld.

Simulation of removing the most central nodes in a graph. Used in this article by Manish Pandey.

In this simulation you can see which nodes are the most central given some centrality measure. You choose a graph generated from the Erdős-Rényi Random Graph, Preferential Attachment, Geometric, and Configuration Model. You can also choose a centality measure from Betweeness, PageRank, Closeness, Harmonic, Degree, Eigenvector, and Game of Thieves centrality.Simulation of a simple voter model on a CM network. The visualization also shows the network generation phase. Used in this article by Federico Capannoli. Several variations on other graphs:

Animation of the flow on a network, this includes advanced methods to read the flow and network data from input files.
You can find the animation here.

Animation showing how Szemerédi's regularity result works. If you want to read more about this you can have a look at the article of Jop Briët.

Funny animations of queueing systems which can be used for illustrative purposes.

A simulation where you can try out boarding orders and see how they affect the total time it needs for all passengers to get on the plain. Do you think the best way could be implemented in an actual airport?

Simulation of wireless queueing model with interference. The animation (developed by Thom Castermans from the Eindhoven University of Technology)
shows the behaviour of a wireless network when implementing two different random-access protocols. On the left the network follows a protocol with
fixed activation rates, while on the right it follows a queue-based protocol. The histograms help to keep track of the size of the queues in the network.
Used in this article by Matteo Sfragara.

In this simulation we show how an infection can spread on the nodes of the network. Yellow represents susceptible nodes,
green represents immune nodes, and red represents infected nodes. You can choose yourself the vaccination rate.

In this simulation we generate the connections of a random network shown on a grid. We show how an infection can spread on the nodes of the network.