This is a genuine 2D counterpart: the network is laid out with a real 2D force-directed physics engine (Coulomb-like node repulsion F ∝ 1/d plus Hookean spring attraction along surviving edges, both computed directly in the plane — not a projection of a 3D scene), and a second live chart tracks the percolation curve S(f): the giant-component fraction as a function of the fraction f of proteins removed.
repulsion: F = k_r / d² (per pair, alive nodes only)
spring: F = k_s · (d − L₀) (per surviving edge)
integrate: v += (F/m)·dt; v *= damping; x += v·dt
The chart also draws the analytic Molloy–Reed / Cohen et al. (2000) prediction for the critical removal fraction at which a configuration-model random network's giant component vanishes under uniform random node removal:
κ = ⟨k²⟩ / ⟨k⟩ (computed from the intact network's degree sequence)
f_c = 1 − 1/(κ − 1)
Because targeted hub removal deletes far more edges per node than random removal, its empirical collapse point falls well below this random-removal prediction — the same "robust yet fragile" asymmetry the 3D companion sim shows, here made quantitative with a live number instead of only a visual read.
- Random Failure — each step removes a uniformly random surviving protein.
- Targeted Attack — each step removes the surviving protein with the highest current degree.
- κ is recomputed once per network build from the intact degree sequence (Molloy–Reed theory is defined on the original topology, not the damaged one).
- The dashed vertical line on the chart marks the predicted fc; the small diamond marks where the simulated giant component actually first drops below 10% of surviving nodes.