convergence bound for graph convolutions
[[concept]]
Let
Further, assume
(WLOG) and is Lipschitz in and Lipschitz in
Then we have
note that this bound depends on
- the norm difference between the graphon and the induced graphon
- the the norm difference between the graphon signal and the induced graphon signal
We already know that these two things converge (we've already discussed the (slow) convergence rate for
- Convergence
with appropriate node labelling means approximation improves with as expected - The bound grows with
- if we want better discriminability, then we need a lower
. A lower means - a higher c band cardinality
- a lower c eigenvalue margin
- a higher
, and thus a worse convergence bound
- a higher c band cardinality
- ie, the bound is large when the filter is most discriminative
- if we want better discriminability, then we need a lower
Here, the convergence-discriminability tradeoff is explicit. We can see that larger
The last term
In the finite sample regime, unless
Review
What 3(or 4) conditions do we need for a convergence bound on the graph convolution
-?-
- The graphon signal
- the functions
and is Lipschitz in - and
Lipschitz in
- and
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File | Last Modified |
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2025-04-07 lecture 18 | 2025-05-30 |
Created 2025-04-22 Last Modified 2025-05-30