convergence bound for graph convolutions

[[concept]]
Theorem

Let (Gn,Xn)(W,X) and let graph and graphon convolutions be such that

H(Gn)=k=0hk(Ann)k and TH=k=0hkTWk

Further, assume

  1. ||X||1 (WLOG)
  2. |h(λ)|<1 and h(λ) is LLipschitz in [1,c][c,1] and Lipschitz in (c,c)

Then we have

||YnY||(L+πncδc)||TWTWn||+(Lc+2)||XXn||+2c
Note

note that this bound depends on

We already know that these two things converge (we've already discussed the (slow) convergence rate for Wn in induced graph sequence converges if and only if the induced graphon sequence converges). We can bring the rates for both quantities to O(1n) by introducing more restrictive requirements.

Here, the convergence-discriminability tradeoff is explicit. We can see that larger L and smaller c (more discriminative filters) lead to a higher error bound.

The last term 2c does not vanish. We can think of this as the "approximation error" that comes from the fact that the eigenvalues of the graphs converge non-uniformly (recall this conjecture).

In the finite sample regime, unless =0, there is always leftover "nontransferable energy" corresponding to the spectral components with |λi|<c which do not converge

Review

#flashcards/math/dsg

What 3(or 4) conditions do we need for a convergence bound on the graph convolution H(Gn)=k=0hk(Ann)k to the limiting graphon convolution TH=k=0hkTWk of a signal XX?
-?-

  1. The graphon signal ||X||1
  2. the functions |h(λ)|<1 and
  3. h(λ) is LLipschitz in [1,c][c,1]
    • and Lipschitz in (c,c)

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2025-04-07 lecture 18 2025-05-30

Created 2025-04-22 Last Modified 2025-05-30