New curvature concept preserves graph distances under operations.
problem Preserving graph distances under graph operations.
method Characterization of distance matrix and its null space.
result Linear system may not have a solution.
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43 results for “graph-distance”
Using existing technology, we prove a Masur-Minsky style distance formula for flip- graph distance between two triangulations, expressed as a sum of the distances of the projections of these triangulations into arc graphs of the suitable subsurfaces of S.
Graph neural network learns graph distances effectively.
problem Maintaining graph distance metric properties.
method GRAPH-BERT based semi-supervised distance metric learning.
result GB-DISTANCE outperforms existing methods.
New bounds for average graph distance using curvature and centrality.
problem Finding bounds for average graph distance.
method Using weighted average Ollivier curvature with edge betweenness centrality.
result Equality in bounds achieved for specific reflective graphs.
Proposes Isometric Graph Neural Networks to preserve graph distances.
problem Lack of faithful distance representation in graph neural networks.
method Introduces a new technique to modify GNNs' input space and loss function.
result Significant improvement in reflecting graph distances, as measured by KT.
New methods cluster and test graphs without vertex correspondence.
problem Clustering and testing of networks without vertex correspondence.
method Inspired by graphon estimation, propose a novel graph distance and clustering algorithms.
result Prove statistical consistency of clustering algorithms under Lipschitz assumptions on graph degrees.
Paper develops methods for semi-supervised Fréchet regression.
problem High costs of obtaining non-Euclidean labels.
method Proposes semi-supervised NW Fréchet regression and semi-supervised kNN Fréchet regression.
result Demonstrates superior performance over supervised methods.
Researchers create a framework to value player actions in CSGO.
problem Lack of accessible data and analytical frameworks for esports players.
method Data model, graph distance measure, context-aware framework.
result Demonstrated framework's consistency and independence compared to existing methods.
Landmark-Based Node Representations for Shortest Path Distance Approximations in Random Graphsstat.ML
Landmark-based node embeddings approximate shortest path distances in random graphs.
problem Capturing global graph distances in node representations.
method Landmark-based node embeddings using shortest path distances from a subset of reference nodes (landmarks).
result Random graphs require lower dimensions in landmark-based embeddings compared to worst-case graphs.
The study of networks has received increased attention recently not only from the social sciences and statistics but also from physicists, computer scientists and mathematicians. One of the principal problem in networks is community detection. Many algorithms have been proposed for community finding but most of them do…
New method trains neural networks faster with fewer examples.
problem Training neural networks efficiently with limited data.
method Uses multilevel methods and graph-distance metrics for optimization.
result MsANN training achieves comparable error with fewer training examples.
New neural nets respect triangle inequality, improving graph and reinforcement learning performance.
problem Neural nets lack inductive bias for certain subadditive distances.
method Introduced novel architectures that universally approximate norm-induced metrics.
result Neural nets with triangle inequality inductive bias outperform existing approaches.
If a graph is in bridge position in a 3-manifold so that the graph complement is irreducible and boundary irreducible, we generalize a result of Bachman and Schleimer to prove that the complexity of a surface properly embedded in the complement of the graph bounds the graph distance of the bridge surface. We use this r…
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and Morecs.LG
Two log-linear approximations speed up optimal transport for deep learning applications.
problem Computing optimal transport in high dimensions is computationally expensive.
method Locality-sensitive hashing (LSH) and Nyström approximation with LSH-based sparse corrections.
result Log-linear time algorithms for entropy-regularized OT perform well in high-dimensional spaces.
A new graph kernel uses LCS and Wasserstein distance for better graph comparisons.
problem Graph learning methods can be limited by information from distant vertices and path length constraints.
method Proposes a Graph Kernel based on LCS similarity and Wasserstein distance in a novel metric space.
result The new kernel emphasizes comparisons between similar paths and reduces information loss.
Unsupervised neural network learns graph embeddings for various tasks.
problem Efficiently representing and comparing families of graphs for mining tasks.
method An unsupervised neural network approach to learn graph embeddings.
result Our method outperforms graph distances and kernels in clustering and classification tasks.
New graph distance measures derived from graph Laplacian optimization.
problem Developing new graph similarity and distance metrics.
method Optimization of graph Laplacian exponential matrices under constraints.
result Efficient calculation of graph distance metrics with upper bounds.
GSimCNN predicts graph similarity using CNNs, outperforming existing methods.
problem Challenging pairwise graph similarity computation due to NP-hardness.
method Graph Edit Distance (GED) as core metric, GSimCNN (Convolutional Neural Networks).
result State-of-the-art performance on graph similarity search.
Algorithm reconstructs vertex positions in random geometric graphs with improved accuracy.
problem Reconstructing vertex positions in random geometric graphs with high accuracy.
method Hybrid of graph distances and short-range estimates based on common neighbors.
result Algorithm reconstructs vertex positions with error of , improving over previous results.
The paper tightens bounds on distances between Reeb graphs.
problem Certifying quasi-universality of distances between Reeb graphs.
method Establishes tight bi-Lipschitz bounds for various distances.
result Proves strict universality of the functional contortion distance for contour trees and coincides with interleaving distance for merge trees.
Conformal Risk Controlstat.ME
Extends conformal prediction for controlling expected risk of monotone loss functions.
problem Controlling expected risk of monotone loss functions.
method Generalizes split conformal prediction with coverage guarantee, extending to distribution shift, quantile risk, multiple, adversarial, and expectations of U-statistics.
result Tight up to an factor, with worked examples in computer vision and natural language processing.
Extends Teichmüller space quasi-isometry to -multicurve graphs.
problem Quasi-isometric relationship between Teichmüller space and curve graphs.
method Electrifying Teichmüller space along thin part, adapting Lackenby-Yazdi pants graph bounds.
result Quasi-isometry of -multicurve graph to electrified Teichmüller space.
COPT optimizes graph distances via simultaneous optimal transport.
problem Learning graph representations unsupervisedly.
method Simultaneous optimization of dual transport plans between vertices and graph signals.
result COPT preserves spectral information and outperforms state-of-the-art methods.
Extends graph encoder embedding to weighted graphs and matrices.
problem Classifying vertices in various graph types efficiently.
method Graph encoder embedding applied to weighted graphs, distance matrices, and kernel matrices.
result The method achieves asymptotic normality, enabling optimal classification.
GRNF embeds graphs into vectors preserving distances.
problem Representing graph data in a vector space while preserving distances.
method Graph Random Neural Features (GRNF) using graph neural networks.
result GRNF preserves graph metric structure and distances.
New framework relaxes independence assumption for graph-mixing dependencies.
problem Tackles limitations of existing generalization results for graph-mixing dependencies.
method Proposes a framework where dependencies decay with graph distance, derives generalization bounds leveraging online-to-PAC framework.
result Derives high-probability generalization guarantees that depend on mixing rate and graph's chromatic number.
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detectioncs.SI
Many modern datasets can be represented as graphs and hence spectral decompositions such as graph principal component analysis (PCA) can be useful. Distinct from previous graph decomposition approaches based on subspace projection of a single topological feature, e.g., the Fiedler vector of centered graph adjacency mat…
Maps with a single face converge to hyperbolic surfaces in large genus.
problem Understanding geometric properties of high genus maps.
method Analyzing uniformly random maps and their convergence to hyperbolic surfaces.
result Lengths of simple cycles converge to a Poisson process.
3-manifolds can be embedded almost in the spine of a trisected 4-manifold.
problem Embedding 3-manifolds in trisected 4-manifolds.
method Defining and using the spine of a trisected 4-manifold, isotoping 3-manifolds to lie in the spine, and calculating bounds based on graph distances.
result Every 3-manifold can be embedded almost in the spine of a minimal genus trisection of connect sums of .
Unified pipeline classifies time series using complex networks and persistent homology.
problem Classifying univariate time series using various graph constructions and metrics.
method Time series to graph, graph to dissimilarity matrix, filtration to persistence diagrams, vectorization to features.
result Persistence-based features are robust to noise and optimal graph type depends on signal structure.
A novel method for comparing graphs of different sizes using Wasserstein distance.
problem Comparing non-aligned graphs of varying sizes.
method Optimal transport in graph comparison framework, solving a one-to-many assignment problem.
result Significant improvements in graph alignment and classification tasks.
Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets. Although there are some redundant edges or…
Improves molecular activity prediction using graph convolutional neural networks considering graph distances.
problem Predicting molecular activity using graph convolutional neural networks with improved distance representation.
method Proposed three improvements: modified graph distances, distance-dependent weight matrices, and weighted sum conversion.
result The proposed method slightly outperforms the original weave module in compound activity prediction.
The problem of learning tree-structured Gaussian graphical models from independent and identically distributed (i.i.d.) samples is considered. The influence of the tree structure and the parameters of the Gaussian distribution on the learning rate as the number of samples increases is discussed. Specifically, the error…
Improved upper bound for discrete isometric filling of cycles.
problem Finding the minimum number of vertices in a discrete isometric filling of cycle graphs.
method Explicit construction of isometric fillings using concentric annular structures.
result Explicit construction of isometric fillings with \( |V(K_n)| \le \left(\frac{1}{6} + o(1)
ight)n^2 \), improving the upper bound to \( D^* \le \frac{1}{6} \).
FSD-CAP: Fractional Subgraph Diffusion with Class-Aware Propagation for Graph Feature Imputationcs.LG
FSD-CAP improves graph feature imputation under high missing rates.
problem Challenges in imputing missing node features in graphs, especially under high missing rates.
method Two-stage framework: subgraph expansion, fractional diffusion, class-aware propagation.
result Significantly improved imputation quality compared to existing methods, achieving high accuracy on benchmark datasets.
This study improves graph coarsening methods by preserving graph spectrum and distances.
problem Solving large-scale graph problems by working on a smaller graph.
method Developed a geometric approach using Gromov--Wasserstein distance to minimize the difference between graph distances and their coarsened versions.
result Minimizing the difference between graph distances and their coarsened versions can be achieved using the weighted kernel -means method.
Proves continuum limits of Lipschitz learning using Γ-convergence.
problem Semi-supervised learning with graph-based methods and continuum limits of -Laplacian learning.
method Proves continuum limits of Lipschitz learning using Γ-convergence.
result Proves -convergence in the -topology to the supremum norm of the gradient.
New method beats volumetric barrier for manifold recovery.
problem Reconstructing latent geometry from noisy distances.
method Orthogonal Ring Distance Estimation Routine (ORDER).
result Achieves pointwise distance estimation of order .
New graph distances derived from optimal transport framework using path flows.
problem Develop new graph distances for clustering and classification.
method Bag-of-paths framework with Gibbs-Boltzmann distribution and optimal transport relaxation.
result Interpolates between shortest-path and resistance distances, improving performance.
A novel approach to computing barycenters on graph-supported probability measures.
problem Computing weighted averages of measures on graphs.
method Dynamic optimal transport formulation on the simplex, gradient descent on the probability simplex.
result Intrinsic gradient descent provides a coherent framework for synthesizing and analyzing measures on graphs.
The paper studies Ricci flow on graphs with prescribed curvature.
problem Characterizing weight evolution on graphs with prescribed curvature.
method Ricci flow with Lin-Lu-Yau curvature prescription.
result Ricci flow converges to weights of prescribed curvature under certain conditions.
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learningcs.LG
DE improves GNNs by distinguishing graph substructures, enhancing accuracy.
problem Limited expressive power of GNNs in representing graph substructures.
method Introduces Distance Encoding (DE) to assist GNNs in distinguishing graph substructures.
result DE distinguishes graph substructures that traditional GNNs cannot, improving accuracy.