New MLG kernels account for multi-scale graph structures.
problem Existing graph kernels are either local or global, ignoring multi-scale structures.
method Builds a hierarchy of nested subgraphs and uses Feature Space Laplacian Graph kernels.
result MLG kernels can capture structure at multiple scales.
Hashing converts continuous graph attributes to discrete labels for scalable graph kernel computation.
problem Handling graphs with continuous attributes using scalable kernels.
method Hash graph kernels derived from discrete kernels using randomized hash functions.
result Hash graph kernels are scalable and effective for graphs with continuous attributes.
Survey on graph kernels for graph classification tasks.
problem Graph classification tasks on graphs.
method Categorization and experimental evaluation of graph kernels.
result Simple baselines become competitive after Gaussian RBF kernel transformation on some datasets.
Graph kernels assess graph similarity for various applications.
problem Assessing similarity between graphs for predictions.
method Review and comparison of existing graph kernels.
result State-of-the-art graph kernels reviewed and compared.
Proposes a method to learn a low-rank kernel matrix for graph-based clustering.
problem Challenges in learning an optimal kernel matrix for graph-based clustering.
method Unified framework for graph construction and kernel learning, focusing on a low-rank kernel matrix.
result Efficacy of the proposed method validated through extensive experiments.
Develops multi-kernel regression for graph signal processing.
problem Smoothness of graph signals over a graph.
method Estimates linear weights to learn effective kernel function using graph smoothness.
result Optimization problem is convex and accelerated projected gradient descent solution proposed.
Graph convolutional kernel networks generalize CNNs to graph data.
problem Representing graph-structured data for machine learning.
method Convolutional kernel networks applied to graph data.
result Competitive performance on graph classification benchmarks.
New graph kernel scales well with graph size and number, achieving state-of-the-art performance.
problem Graph kernels lose structure information when representing graphs.
method Proposes a positive-definite global alignment graph kernel using random features and random graph embeddings.
result Achieves quasi-linear scalability with respect to graph size and number.
New graph kernels capture spatio-temporal interactions.
problem Lack of justified spatio-temporal graph kernels for graph problems.
method Derive graph kernels via SPDEs for spatio-temporal modelling.
result Non-separable spatio-temporal graph kernels outperform existing ones.
This paper reconstructs complex graph signals using kernel methods on manifolds.
problem Reconstructing complex graph signals from samples on graph vertices.
method Kernel methods on complex manifolds, embedding vertices into higher-dimensional spaces.
result Effective reconstruction of complex graph signals, outperforming conventional methods.
Tree++ graph kernel captures similarities at multiple granularities.
problem Lack of scale-adaptivity in existing graph kernels.
method Tree++ uses truncated BFS trees and super paths to represent graphs at different granularities.
result Tree++ achieves best classification accuracy on real-world graphs.
New graph kernels for evolving graphs with ordered neighborhoods.
problem Graphs with evolving edges over time.
method Combining convolutional subgraph kernels and string kernels, new scalable algorithms for generating graph feature maps.
result Neighborhood ordering yields more informative features.
Graph kernels improve graph similarity and learning tasks.
problem Tackling graph similarity and learning tasks.
method Proposes a message passing scheme framework for designing graph kernels.
result Kernels derived from the framework are competitive with state-of-the-art methods.
Graph kernels survey covers various types and applications.
problem Learning on structured data, focusing on graph properties.
method Comprehensive overview and experimental evaluation of graph kernels.
result Graph kernels have proven successful across multiple domains.
Develops graph kernels using random walk return probabilities.
problem Quantifying similarities among graphs.
method Graph kernels based on return probabilities of random walks.
result Significantly outperform existing graph kernels in accuracy and efficiency.
We introduce a new graph kernel combining local and global properties.
problem Graph kernels focusing on local properties often fail on large graphs.
method Weisfeiler-Lehman algorithm with stochastic approximation.
result Our kernel outperforms state-of-the-art on graph classification benchmarks.
Graph kernels for metric graphs using tropical algebra.
problem Comparing graphs representing different metric spaces.
method Purely based on geometry and topology, invariant under edge subdivision.
result Capture complementary geometric and topological information.
Graph kernel uses Ricci curvature for comparison.
problem Graph comparison without node attributes.
method Edge curvature distribution for graph kernel.
result Graphs can be compared using topology alone.
New framework for node classification on graphs using kernel methods.
problem Graph kernel methods for node classification are ill-posed and rely on heuristics.
method Theoretical kernel-based framework for node classification, combining graph kernel methodology with node feature aggregation and data-driven similarity metrics.
result Our framework sets a new state of the art in node classification benchmarks.
DHGAK aligns substructures for better graph kernel performance.
problem Limited performance of traditional graph kernels due to missing substructure similarities.
method Hierarchically aligns relational substructures in deep embedding space, assigning same feature maps in RKHS.
result DHGAK outperforms state-of-the-art graph kernels on various benchmarks.
New kernel improves graph learning with fewer labeled data.
problem Limited kernels for node-level problems on graphs.
method Derived from a regularization framework, transductive kernel for graphs with node features.
result Improved learning on fewer training points and non-Euclidean data.
Graph Neural Tangent Kernels combine GNNs and GKs for better graph classification.
problem Limited expressive power of graph kernels and difficulties in training graph neural networks.
method Graph Neural Tangent Kernels (GNTKs) are infinitely wide multi-layer GNNs trained by gradient descent.
result GNTKs achieve strong performance on graph classification datasets.
We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al., 2009) in general can not be applied to attributed graphs, our approach allows to rate mappings of subgraphs by …
This review introduces graph kernels for chemoinformatics.
problem Quantifying similarity between molecular graphs.
method Graph kernels as a method for quantifying molecular graph similarity.
result Graph kernels have direct applications in chemoinformatics.
A new kernel measures brain network similarities, improving disease classification.
problem Lack of edge weight information in existing graph kernels for brain connectivity networks.
method Ordinal pattern kernel for weighted brain connectivity networks.
result The ordinal pattern kernel achieves better classification performance than state-of-the-art graph kernels.
GraKeL combines multiple graph kernels for graph similarity measurement.
problem Accurately measuring graph similarity across various applications.
method Unified graph kernel library in Python with scikit-learn interface.
result Facilitates graph classification and clustering tasks.
End-to-end graph SVM with graph convolutions and RKHS.
problem Graph classification with complex feature spaces.
method End-to-end training of graph convolutions, kernel function, and SVM parameters.
result Outperforms existing deep learning models on graph classification tasks.
Graph kernels on time series improve ASD classification.
problem Classifying autism severity from fMRI time series data.
method Use graph kernels to model spatio-temporal dynamics of brain networks.
result Graph kernels outperform traditional kernels in ASD classification.
A novel algorithm for unbiased graph kernel estimation with subquadratic time complexity.
problem Efficient estimation of graph kernels for large networks.
method Random walk-based algorithm with modulation function parameterized by neural network.
result Higher-quality kernel estimates and efficient scalable learning on larger networks.
Pre-trains GNNs using graph kernels for improved performance.
problem Low accuracy of GNNs in various tasks.
method Task-independent pre-training using graph kernels.
result Consistent improvements in GNN predictive performance.
Sharp heat kernel estimate on graphs proved.
problem Estimating heat kernels on graphs.
method Proved sharp Davies-Gaffney-Grigor'yan lemma.
result Sharp estimate of heat kernels on graphs.
Proposes a Gaussian process for graph signals using adaptive spectral kernels.
problem Predicting signals on graph nodes with various structures.
method Spectral kernel learning approach that incorporates a polynomial function in the graph spectral domain.
result The model accurately recovers ground truth spectral filters and outperforms in real-world graph data.
Kernel regression improves graph signal estimation.
problem Estimating graph signals from noisy observations.
method Kernel regression in reproducing kernel Hilbert spaces.
result Kernel methods offer richer prior information and simpler estimators.
Improved graph kernel performance with structural variation noise.
problem Effect of structural variation on graph kernel predictive performance.
method Introduced a noise-robust adaptation of the GraphHopper kernel and validated it on benchmark data.
result Obtained modestly improved predictive performance on a range of datasets.
A new graph kernel uses Wasserstein distance for better graph comparison.
problem Graph kernels often discard valuable information and struggle with continuous attributes.
method Proposes a novel graph kernel using Wasserstein distance for node feature vector distributions.
result Improves prediction performance on graph classification tasks.
New kernel speeds up graph regression in physics.
problem Handling large, sparse graphs with continuous node attributes in physics.
method Introduced Sliced Wasserstein Weisfeiler-Lehman (SWWL) graph kernel for Gaussian process regression.
result The SWWL kernel is efficient and positive definite, reducing complexity.
Unified feature maps for graph kernels improve efficiency without sacrificing accuracy.
problem Efficiently applying non-linear kernel methods to large-scale graph data.
method Constructing feature maps for graph kernels, analyzing feasibility, and proposing algorithms.
result Explicit feature maps can achieve similar accuracy to kernel trick methods but with significantly reduced computation time.
A faster graph kernel using optical random features.
problem High computation cost of graphlet kernel due to isomorphism test.
method Kernel random features, optical random features, mean kernel metric.
result The proposed method is orders of magnitude faster with similar or better accuracy.
We introduce propagation kernels, a general graph-kernel framework for efficiently measuring the similarity of structured data. Propagation kernels are based on monitoring how information spreads through a set of given graphs. They leverage early-stage distributions from propagation schemes such as random walks to capt…
Graph-based kernels improve GP performance on graph data.
problem Improving Gaussian process performance on graph-structured data.
method Introduced graph neural network-inspired kernels into Gaussian processes.
result Graph convolutional networks are equivalent to certain GP kernels when infinitely wide.
LGKDE learns graph density using neural networks and perturbations.
problem Graph density estimation challenges in capturing structural patterns and semantic variations.
method LGKDE uses graph neural networks to represent graphs as discrete distributions and learns graph metrics via maximum mean discrepancy.
result LGKDE outperforms state-of-the-art baselines in graph anomaly detection.
A new kernel for ranked data tackles computational challenges.
problem Complex geometric structure and partial rankings make existing algorithms infeasible for real-world applications.
method Derives a graph cut kernel that combines submodular optimization and kernel-based methods.
result The graph cut kernel efficiently handles large-scale ranked data.
We propose a representation of graph as a functional object derived from the power iteration of the underlying adjacency matrix. The proposed functional representation is a graph invariant, i.e., the functional remains unchanged under any reordering of the vertices. This property eliminates the difficulty of handling e…
Deep Divergence Graph Kernels learn graph representations without supervision.
problem Learning graph representations without feature engineering or labeled graphs.
method Unsupervised method using cross-graph attention networks and divergence scores.
result Learned representations achieve competitive results on graph classification tasks.
Predicts atomization energy with high accuracy using graph kernels and active learning.
problem Predicting molecular atomization energy with high accuracy.
method Gaussian process regression with marginalized graph kernel, active learning.
result Achieves mean absolute error of 0.62 +- 0.01 kcal/mol with 2000 training samples.
Paper develops a graph-based method for reconstructing spatio-temporal signals.
problem Reconstructing space-time varying signals on graphs given limited data.
method Multi-kernel Kriged Kalman Filter combining graph-aware kernels and online selection.
result Superior reconstruction performance compared to existing methods.
New kernel improves graph classification accuracy.
problem Improving graph classification accuracy.
method Developed an optimal assignment kernel for graphs.
result Improved graph classification accuracy on benchmark data.
Study evaluates RKHS choices for assessing graph models using KSD tests.
problem Effect of RKHS choice on KSD tests for graph model assessment.
method Investigated power performance and computational runtime of KSD tests for ERGMs and synthetic graph generators.
result Different RKHS choices affect KSD test performance and computational runtime.