| Baire Category Theorem |
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2025-06-05 |
2025-06-05 |
| bijective bounded linear operators have bounded linear inverses |
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2025-06-05 |
2025-06-05 |
| closed subspaces of banach spaces are banach |
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2025-06-05 |
2025-06-05 |
| convergent graph sequence |
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2025-06-05 |
2025-03-24 |
| equivalence relation |
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2025-06-05 |
2025-06-05 |
| fully random graphs converge to the graphon in probability |
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2025-06-05 |
2025-03-26 |
| graphon shift operator |
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2025-06-05 |
2025-03-31 |
| graphon |
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2025-06-05 |
2025-03-24 |
| open mapping theorem |
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2025-06-05 |
2025-06-05 |
| quotient of a vector space |
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2025-06-05 |
2025-06-05 |
| the cartesian product of banach spaces is banach |
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2025-06-05 |
2025-06-05 |
| uniform boundedness theorem |
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2025-06-05 |
2025-06-05 |
| ways to sample graphs from graphons |
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2025-06-05 |
2025-03-26 |
| closed graph theorem |
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2025-06-05 |
2025-06-05 |
| partial order |
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2025-06-05 |
2025-06-05 |
| upper bound |
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2025-06-05 |
2025-06-05 |
| maximal element |
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2025-06-05 |
2025-06-05 |
| chain |
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2025-06-05 |
2025-06-05 |
| Hamel basis |
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2025-06-05 |
2025-06-05 |
| Zorn's lemma |
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2025-06-05 |
2025-06-05 |
| cut distance |
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2025-06-04 |
2025-03-24 |
| lipschitz graph convolutions of graph signals converge to lipschitz graphon filters |
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2025-06-03 |
2025-04-15 |
| fixed coefficients yield the same spectral response for both graphon and graph convolutions |
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2025-06-02 |
2025-04-09 |
| spectral representation of graphon convolutions |
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2025-06-02 |
2025-04-09 |
| Banach space |
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2025-05-30 |
2025-05-27 |
| Graph Isomorphism Network |
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2025-05-30 |
2025-03-03 |
| bounded linear operator space is banach |
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2025-05-30 |
2025-05-30 |
| c band cardinality |
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2025-05-30 |
2025-04-22 |
| continuous map |
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2025-05-30 |
2025-05-29 |
| convergence bound for graph convolutions |
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2025-05-30 |
2025-04-22 |
| eigenfunction |
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2025-05-30 |
2025-03-31 |
| graph homomorphism |
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2025-05-30 |
2025-03-03 |
| graph motif |
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2025-05-30 |
2025-03-24 |
| graphon fourier transform |
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2025-05-30 |
2025-04-02 |
| graphon shift operators are self-adjoint |
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2025-05-30 |
2025-03-31 |
| graphon signal |
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2025-05-30 |
2025-03-26 |
| template graph |
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2025-05-30 |
2025-03-26 |
| we can write a graphon in the basis of its shift operator |
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2025-05-30 |
2025-04-01 |
| absolutely summable series have Cauchy partial sums |
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2025-05-29 |
2025-05-29 |
| banach spaces have all absolutely summable series are summable |
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2025-05-29 |
2025-05-29 |
| complete metric spaces have banach continuous bounded function spaces |
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2025-05-29 |
2025-05-27 |
| continuous bounded function space |
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2025-05-29 |
2025-05-27 |
| finite vector space |
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2025-05-29 |
2025-05-27 |
| infinity norm for continuous bounded function space |
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2025-05-29 |
2025-05-27 |
| l-p vector space |
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2025-05-29 |
2025-05-27 |
| semi-norm |
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2025-05-29 |
2025-05-27 |
| summable series |
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2025-05-29 |
2025-05-29 |
| weighted graph (sample) |
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2025-05-28 |
2025-03-26 |
| distance |
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2025-05-27 |
2025-03-24 |
| Chebyshev Polynomial |
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2025-05-13 |
2025-02-17 |
| Convergence in the cut norm implies convergence in L2 |
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2025-05-13 |
2025-03-26 |
| Davis-Kahan Theorem |
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2025-05-13 |
2025-04-09 |
| GCN layers can be written as graph convolutions |
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2025-05-13 |
2025-02-20 |
| GINs are maximally powerful for anonymous input graphs |
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2025-05-13 |
2025-03-03 |
| GNNs inherit stability from their layers |
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2025-05-13 |
2025-03-12 |
| GNNs perform better than their constituent filters |
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2025-05-13 |
2025-03-12 |
| Graph Neural Networks |
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2025-05-13 |
2025-02-05 |
| Lipschitz continuous |
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2025-05-13 |
2025-03-05 |
| Lipschitz filters are stable to additive perturbations |
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2025-05-13 |
2025-03-10 |
| MPNNs can be expressed as graph convolutions |
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2025-05-13 |
2025-02-18 |
| We can verify whether graphs without node features and different laplacian eigenvalues are not isomorphic |
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2025-05-13 |
2025-02-21 |
| Weisfeiler-Leman Graph Isomorphism Test |
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2025-05-13 |
2025-02-21 |
| aggregation readout layer |
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2025-05-13 |
2025-02-21 |
| almost exact recovery is impossible when the signal to noise ratio is less than the threshold |
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2025-05-13 |
2025-02-10 |
| analytic function |
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2025-05-13 |
2025-02-03 |
| approximation of heaviside functions using convolutional graph filters |
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2025-05-13 |
2025-02-04 |
| balanced stochastic block model |
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2025-05-13 |
2025-02-10 |
| bandlimited convergent graph signals converge in the fourier domain |
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2025-05-13 |
2025-04-09 |
| bandlimited graphon signal |
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2025-05-13 |
2025-04-09 |
| c eigenvalue margin |
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2025-05-13 |
2025-04-22 |
| chebyshev equioscillation theorem |
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2025-05-13 |
2025-02-20 |
| chebyshev polynomials are orthogonal |
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2025-05-13 |
2025-02-20 |
| color refinement algorithm |
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2025-05-13 |
2025-02-21 |
| compressed sparse row representation |
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2025-05-13 |
2025-02-12 |
| computational graph |
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2025-05-13 |
2025-03-03 |
| conditions for finding a convolutional graph filter |
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2025-05-13 |
2025-01-29 |
| contextual stochastic block model |
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2025-05-13 |
2025-02-12 |
| convergence in L-p implies convergence in cut norm |
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2025-05-13 |
2025-03-26 |
| convolutional filter bank |
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2025-05-13 |
2025-02-05 |
| convolutional graph filters are local |
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2025-05-13 |
2025-01-29 |
| convolutional graph filters are permutation equivariant |
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2025-05-13 |
2025-01-29 |
| convolutional graph filters are shift equivariant |
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2025-05-13 |
2025-01-29 |
| coordinate representation |
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2025-05-13 |
2025-02-12 |
| cut norm |
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2025-05-13 |
2025-03-24 |
| cycle homomorphism density is given by the trace of the adjacency matrix |
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2025-05-13 |
2025-03-04 |
| discriminability of a graph filter |
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2025-05-13 |
2025-03-10 |
| eigenvalues of the induced graphon converge pointwise to the eigenvalues of the limit |
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2025-05-13 |
2025-04-02 |
| eigenvector misalignment |
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2025-05-13 |
2025-03-10 |
| feature-aware spectral embeddings |
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2025-05-13 |
2025-02-12 |
| filter permutation invariance |
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2025-05-13 |
2025-03-10 |
| fully connected readout layer |
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2025-05-13 |
2025-02-19 |
| fully random graph |
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2025-05-13 |
2025-03-26 |
| graph SAGE |
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2025-05-13 |
2025-02-20 |
| graph attention model |
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2025-05-13 |
2025-02-19 |
| graph automorphism |
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2025-05-13 |
2025-03-05 |
| graph convolution |
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2025-05-13 |
2025-01-29 |
| graph convolutional network |
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2025-05-13 |
2025-02-20 |
| graph convolutions are stable to perturbations in the data and coefficients |
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2025-05-13 |
2025-03-05 |
| graph edit distance |
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2025-05-13 |
2025-03-24 |
| graph isomorphism is not solvable in polynomial time |
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2025-05-13 |
2025-03-02 |
| graph isomorphism |
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2025-05-13 |
2025-02-21 |
| graph perceptron |
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2025-05-13 |
2025-02-05 |
| graph sequence converges if and only if the induced graphon sequence converges |
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2025-05-13 |
2025-03-26 |
| graph shift operator |
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2025-05-13 |
2025-01-22 |
| graph signal processing problem |
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2025-05-13 |
2025-02-04 |
| graph signals |
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2025-05-13 |
2025-01-22 |
| graph-level problem |
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2025-05-13 |
2025-02-04 |
| graph |
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2025-05-13 |
2025-01-22 |
| graphon convolution |
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2025-05-13 |
2025-04-09 |
| graphon shift operator eigenvalues |
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2025-05-13 |
2025-04-01 |
| heaviside functions |
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2025-05-13 |
2025-02-04 |
| homomorphism density |
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2025-05-13 |
2025-03-04 |
| hypothesis class |
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2025-05-13 |
2025-02-04 |
| induced graphon signal |
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2025-05-13 |
2025-03-26 |
| induced graphon |
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2025-05-13 |
2025-03-24 |
| inductive learning |
🫘 |
2025-05-13 |
2025-02-04 |
| information theoretic threshold |
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2025-05-13 |
2025-02-12 |
| integral Lipschitz filter |
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2025-05-13 |
2025-03-10 |
| integral lipschitz filters are stable to dilations |
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2025-05-13 |
2025-03-10 |
| inverse graph fourier transform |
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2025-05-13 |
2025-01-29 |
| kernel cut metric |
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2025-05-13 |
2025-03-24 |
| kernel cut norm |
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2025-05-13 |
2025-03-24 |
| leaky ReLU |
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2025-05-13 |
2025-02-19 |
| linear graph filter |
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2025-05-13 |
2025-01-29 |
| message passing neural network |
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2025-05-13 |
2025-02-18 |
| multi-layer graph perceptron |
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2025-05-13 |
2025-02-05 |
| node-level task |
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2025-05-13 |
2025-02-04 |
| operator distance modulo permutations |
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2025-05-13 |
2025-03-05 |
| quasi-symmetry |
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2025-05-13 |
2025-03-05 |
| random graphs in a gin are good for graph isomorphism |
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2025-05-13 |
2025-03-04 |
| readout layer |
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2025-05-13 |
2025-02-19 |
| relative perturbation edge changes are tied to node degree |
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2025-05-13 |
2025-03-10 |
| relative perturbations |
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2025-05-13 |
2025-03-10 |
| signal to noise ratio |
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2025-05-13 |
2025-02-10 |
| sometimes spectral algorithms fail |
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2025-05-13 |
2025-02-12 |
| spectral clustering |
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2025-05-13 |
2025-02-10 |
| spectral embedding |
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2025-05-13 |
2025-02-12 |
| spectral graph filter |
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2025-05-13 |
2025-02-04 |
| spectral representation of a convolutional graph filter |
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2025-05-13 |
2025-01-29 |
| stability and size tradeoff for realistic sparsity pattern considerations setting |
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2025-05-13 |
2025-03-10 |
| stability-discriminability tradeoff for Lipschitz filters |
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2025-05-13 |
2025-03-10 |
| stable graph filter |
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2025-05-13 |
2025-03-10 |
| statistical risk minimization problem |
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2025-05-13 |
2025-02-04 |
| stochastic block model |
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2025-05-13 |
2025-02-10 |
| template graphs converge to the graphon |
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2025-05-13 |
2025-03-26 |
| the spectral graph filter operates on a signal pointwise |
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2025-05-13 |
2025-01-29 |
| the spectral representation of a graph filter is independent from the graph |
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2025-05-13 |
2025-01-29 |
| we can represent any analytic function with convolutional graph filters |
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2025-05-13 |
2025-02-04 |
| we can use GNNs to solve feature-aware semi-supervised learning problems |
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2025-05-13 |
2025-02-12 |
| weighted sampled graphs converge to the graphon in probability |
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2025-05-13 |
2025-03-26 |
| Hilbert-Schmidt integral operator |
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2025-04-15 |
2025-03-31 |
| equivariant |
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2025-04-15 |
2025-01-30 |
| singular value decomposition |
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2025-04-15 |
2024-10-11 |
| invariant |
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2025-03-26 |
2025-01-30 |
