Percolation · higher-order networks

Percolation on Simplicial Complexes

Percolation on Simplicial Complexes
fig. Percolation on Simplicial Complexes
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The notebook runs on its own once it loads, so give it about 10 seconds while Python starts up in your browser, and the simulations begin animating. Move the sliders and everything recomputes live. The code is shown alongside the output so you can read exactly how it works; the full editable source is linked below.

Ordinary percolation lives on a graph: add links until a giant connected cluster appears. I pushed it up a dimension, onto simplicial complexes where the pieces are triangles and tetrahedra, not just edges. The higher-order versions do not ease into their transition the way ordinary networks do; they snap, much closer to discontinuous. I track the giant component and the susceptibility across the occupation probability and use finite-size scaling to pin down where the jump sits.

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