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)||p≤LCϵ+O(ϵ2)

(stable to dilation/scaling)

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

||Φ(S,h)−Φ(S~,h)||p≤LC(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)||p≤2LC(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. ||xℓ||≤1∀ℓ - 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)||p≤chϵ

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

||x~ℓ−xℓ||=||σ(Hℓ(S~)x~ℓ−1)−σ(Hℓ(S)xℓ−1)||(since σ=1)≤||Hℓ(S~)x~ℓ−1−Hℓ(S~)xℓ−1||=||Hℓ(S~)x~ℓ−1−Hℓ(S~)xℓ−1+Hℓ(S~)xℓ−1−Hℓ(S)xℓ−1||=||Hℓ(S~)[x~ℓ−1−xℓ−1]+[Hℓ(S~)−Hℓ(S)]xℓ−1||(by △ ineq.)≤||Hℓ(S~)||1⋅||x~ℓ−1−xℓ−1||+||xℓ−1||≤1⋅||Hℓ(S)−Hℓ(S~)||≤chϵ(∗)≤||x~ℓ−1−xℓ−1||+chϵ

We can apply the same reasoning to get a similar expression for ||x~ℓ−1−xℓ−1||,||x~ℓ−2−xℓ−2||,… etc for the final expression with LC

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