Self-averaging enables reconstruction of higher-order chaotic networks
Higher-order interactions make reconstructing chaotic networks very hard because there are too many possible interaction terms. This work finds a special case: weakly coupled, higher-order chaotic networks allow for a reduction of these interactions. By focusing on hub nodes and weak coupling, the complex aggregate interaction can be simplified into a compact function of the hub's state, enabling reconstruction through sparse regression.