GNNs inherit stability from their layers

[[concept]]
Theorem

Let Φ(S,h) be an L-layer GNN. Let S~ be a graph perturbation modulo permutations.

(1) if S~=S+ϵS and all filters are integral Lipschitz, then

||Φ(S,h)Φ(S~,h)||pLCϵ+O(ϵ2)

(stable to dilation/scaling)

(2) If PTS~P=SE~ and all h are lipschitz, then

||Φ(S,h)Φ(S~,h)||pLC(1+δn)ϵ+O(ϵ2)

(stable to additive perturbations)

(3) If PTS~P=S+E~S+SE~ and all h are integral Lipschitz, then

||Φ(S,h)Φ(S~,h)||p2LC(1+δn)ϵ+O(ϵ2)

(stable to relative perturbations)

And GNNs perform better than their constituent filters.

Proof

We begin with some non-restrictive additional assumptions:

  1. ||x||1 - normalized input at all layers (easy to achieve with non-amplifying h, ie ||H||=1)
  2. σ activation function/nonlinearity is normalized Lipschitz, ie has a Lipschitz constant of 1.

Let ||E~||=ϵ for any of the three perturbation types. Let filters h be stable to E~
with

||H(S~)=H(S)||pchϵ

For each layer 1L, we have is a graph perceptron with filter H. Then, note that

||x~x||=||σ(H(S~)x~1)σ(H(S)x1)||(since σ=1)||H(S~)x~1H(S~)x1||=||H(S~)x~1H(S~)x1+H(S~)x1H(S)x1||=||H(S~)[x~1x1]+[H(S~)H(S)]x1||(by  ineq.)||H(S~)||1||x~1x1||+||x1||1||H(S)H(S~)||chϵ()||x~1x1||+chϵ

We can apply the same reasoning to get a similar expression for ||x~1x1||,||x~2x2||, etc for the final expression with LC

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2025-03-10 graphs lecture 13
2025-03-24 graphs lecture 14