We can verify whether graphs without node features and different laplacian eigenvalues are not isomorphic

[[concept]]
Theorem

Consider two graphs G and G without node features, with laplacian L=VΛVT and L=VΛVT. Further suppose that there is some λj such that λjλi for all i - ie, L has at least one eigenvalue that is different from L.

Then there exists a graph convolution we can use to verify whether two graphs are NOT isomorphic, ie to test that GG

For original proof, see the paper GNNs are more powerful than you think

Proof

Consider the following single-layer (linear) GNN

y=k=0K1hkLkx()

Define the graph feature x to be white, ie such that E[x^i]=1i=1,,n.

Set hk={1 if k=10otherwise

Then we have

E(y^)=E(VTy)=E(VThkLx)=E(VTVΛVTx)=E(Λx)E(y^)=[λ1λ2λn]

And if there is some λj such that λjλi for all i - ie, L has at least one eigenvalue that is different from L as above, then it suffices to compare

E(1Ty^)=iλi=Tr(L)to E(1Ty^)=iλi=Tr(L)

Since the laplacian is positive semidefinite, the sums will be different if and only if we have at least eigenvalue that is different between them.

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Weisfeiler-Leman Graph Isomorphism Test
graph isomorphism
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