GNTK reveals convergence of GNNs on large graphs.
problem Understanding and optimizing GNNs on large graphs.
method Graph Neural Tangent Kernels (GNTK) and graphons.
result GNTKs converge to graphon NTKs on large graphs, enabling task inference.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,341 papers · 148 categories
Trend · papers per month
48 results for “large graph”
The study estimates curvature on graphs with large girth.
problem Estimating curvature on graphs with large girth.
method Utilized CD and CDE inequalities.
result Curvature estimates on finite graphs with large girth.
PyTorch-BigGraph scales graph embeddings to large graphs.
problem Large graphs with billions of nodes and trillions of edges.
method Graph partitioning, multi-relation embedding system, distributed training.
result Comparable performance on benchmarks, scalable to large graphs.
This paper evaluates LLMs on large graph property estimation tasks.
problem Limited context length of LLMs limits their evaluation on large graphs.
method Developed EstGraph dataset and introduced four tasks for LLMs to estimate large graph properties.
result LLMs perform better on graph property estimation tasks when provided with context-rich prompts based on random walks.
New method for efficient graph learning on large graphs.
problem High memory complexity for graph learning on large, non-sparse graphs.
method Approximate large graphs as intersecting communities, using a new graph regularity lemma.
result Efficient graph learning algorithm with linear memory and time complexity.
New model preserves graph structure in large datasets.
problem Lack of permutation invariance in graph generation models for large graphs.
method Uses graph embeddings to create a scalable generative model.
result Model maintains structure in large graphs without losing invariance.
Snake solves large graph optimization problems with fast proximal steps.
problem Optimization over large unstructured graphs with graph-specific regularization.
method Snake algorithm using random simple paths for proximal gradient steps.
result Convergence proven for the Snake algorithm.
CoSimGNN improves graph similarity computation for large graphs.
problem Efficiently computing graph similarity scores for large graphs.
method Embedding-coarsening-matching framework with adaptive pooling and fine-grained interactions.
result CoSimGNN achieves best performance in graph similarity computation.
GPNNs improve semi-supervised classification on large graphs.
problem Handling large graphs for semi-supervised classification.
method Alternates local and global graph propagation with partitioning.
result GPNNs achieve similar performance with fewer steps than standard GNNs.
New method for faster graph parameter inference from large random Kronecker graphs.
problem Efficiently infer graph parameters from large random Kronecker graphs.
method Decompose adjacency matrix into signal and noise components, then use denoising and solving approach.
result Proposed method achieves comparable or better performance than existing methods at lower computational cost.
SHAKE-GNN scales GNNs for large graphs with multi-scale representations.
problem Scaling Graph Neural Networks (GNNs) to large graphs.
method SHAKE-GNN uses a hierarchy of Kirchhoff Forests for stochastic multi-resolution graph decompositions.
result SHAKE-GNN achieves competitive performance on large-scale graph classification benchmarks.
Parallelizes graph embedding for large graphs.
problem Large graphs make existing graph embedding techniques inefficient.
method Distributed parallel computation framework using a cluster of compute nodes.
result Parallel computation scales well and maintains embedding quality.
GLACE embeds large-scale attributed graphs effectively, preserving structure and attributes.
problem Uncertainty and complexity in large-scale attributed graphs.
method Gaussian embeddings for scalable and efficient graph embedding.
result GLACE outperforms state-of-the-art methods on multiple graph analysis tasks.
PPRGo uses approximate PageRank to speed up GNNs on large graphs.
problem Efficiently learning on large graphs using GNNs.
method Approximates PageRank for efficient information diffusion in GNNs.
result PPRGo outperforms other methods in speed and scalability.
FastGAE scales graph AE and VAE to large graphs with millions of nodes.
problem Scalability issues in graph AE and VAE.
method Stochastic subgraph decoding scheme to speed up training.
result Outperforms existing approaches on various real-world graphs.
Graph Convolutional Gaussian Processes predict missing links.
problem Link prediction in large graphs.
method Simplified graph convolutions and variational inducing point method.
result Consistent improvements over existing models and competitive performance.
A scalable graph-based SSL method for large-scale data with few labels.
problem Challenges in semi-supervised learning with limited labeled data and large unlabeled data.
method Constructs a graph from a small set of high-dense vertexes to learn relationships and improve performance.
result Achieves good classification performance, especially with few labels.
The paper investigates why GNNs struggle to generalize from small to large graphs.
problem Challenges in graph neural networks' ability to generalize across different graph sizes.
method Identified and studied the effect of local structure on size generalization; proposed a novel SSL task.
result GNNs can converge to non-generalizing solutions when there is a discrepancy in local structure.
Study of graphs interpolating curve and pants graphs, providing formulae and geometry classifications.
problem Understanding the large-scale geometry of graphs connecting curve and pants graphs.
method Developed explicit formulae for quasi-flat ranks and classified geometries using twist-free graphs of multicurves.
result Explicit formulae for quasi-flat ranks and classification of geometries into hyperbolic, relatively hyperbolic, and thick cases.
BGRL learns graph representations without costly negative examples.
problem Efficient representation learning on large graphs without labels.
method Bootstrapped Graph Latents (BGRL) learns by predicting augmentations.
result BGRL achieves state-of-the-art performance with 2-10x memory savings.
We present a parallelized bijective graph matching algorithm that leverages seeds and is designed to match very large graphs. Our algorithm combines spectral graph embedding with existing state-of-the-art seeded graph matching procedures. We justify our approach by proving that modestly correlated, large stochastic blo…
The paper explores how graph-based semi-supervised learning algorithms behave in large data and noiseless conditions.
problem Understanding semi-supervised learning algorithms in the large graph limit and zero noise scenario.
method The study uses graph Laplacian scaling and various optimization and Bayesian approaches to find continuum limits of these algorithms.
result Conditions are identified for well-defined continuum limits of semi-supervised learning problems.
New graph tests improve on existing methods for comparing large graphs.
problem Comparing large graphs from different sources.
method Proposed new tests based on asymptotic distributions.
result New tests are computationally less expensive and more reliable.
This paper develops a coreset method for GNNs that speeds up training on large graphs.
problem Training Graph Neural Networks (GNNs) on large-scale graphs is computationally expensive.
method The paper proposes a spectral greedy coreset (SGGC) method that selects ego-graphs based on spectral embeddings.
result SGGC significantly speeds up GNN training on large graphs and outperforms other coreset methods.
The paper introduces a sampling theory for graphons with a Poincaré inequality and proves consistency.
problem Sampling on large graphs is challenging due to their non-Euclidean nature.
method The paper introduces a signal sampling theory for graphons, proving a Poincaré inequality and showing consistency.
result Unique sampling sets for graphon signals are consistent across graph sequences.
Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularizationstat.CO
Bayesian method predicts labels on large graphs using Laplacian eigenfunctions.
problem Binary classification on large graphs.
method Hierarchical Bayesian approach with truncated Laplacian regularization.
result Improved scalability for large graphs compared to untruncated Laplacian.
New findings on hyperbolicity of fine curve graphs and their subgraphs.
problem Investigating hyperbolicity of fine curve graphs and their subgraphs.
method Analyzing large subgraphs of fine curve graphs and computing distances in specific cases.
result Large subgraphs of fine curve graphs contain flats of every finite dimension, indicating they are not hyperbolic.
GCN improved for large graphs with LCF to reduce complexity and noise.
problem Efficiency and effectiveness of graph convolution in large graphs.
method Proposed Low-pass Collaborative Filter (LCF) to simplify graph convolution.
result Significant improvement in effectiveness and efficiency of GCN.
Unified framework sparsifies GNNs for faster inference on large graphs.
problem Space and computational bottlenecks in GNNs due to graph size and connectivity.
method Unified GNN sparsification (UGS) framework that prunes graph adjacency matrix and model weights.
result Graph lottery tickets (GLTs) can be trained in isolation to match full model performance.
A new method matches moments exactly for large graphs, improving spectral learning.
problem Lack of exact moment matching in spectral density approximations for large graphs.
method Maximum Entropy method for spectral density approximation, with a new algorithm.
result The new method outperforms existing approaches in learning graph spectra.
Graph Neural Networks struggle on random graphs without node identifiers.
problem Graph Neural Networks' limitations on random graphs without node identifiers.
method Study of Graph Neural Networks and Structural Graph Neural Networks convergence on large random graphs.
result Structural Graph Neural Networks are more powerful and universal than Graph Neural Networks on random graphs.
PASCO speeds up graph clustering for large graphs.
problem Efficiently clustering large graphs with many communities.
method Overlay method combining coarsening and parallel clustering.
result PASCO accelerates clustering with improved efficiency and quality.
GraphSAC detects anomalies in large graphs by sampling and filtering node subsets.
problem Vulnerability of holistic anomaly detection methods to compromised nodal attributes and network links.
method Randomly draws subsets of nodes, filters out contaminated sets, and uses SSL to estimate nominal label distributions.
result GraphSAC provides performance guarantees and is scalable to large graphs.
NeuroMatch efficiently matches subgraphs in large graphs using neural networks.
problem Determining the presence and location of a query graph in a large target graph.
method NeuroMatch decomposes graphs into subgraphs, embeds them using graph neural networks, and matches them directly in the embedding space.
result NeuroMatch is 100x faster and 18% more accurate than existing methods.
This work analyzes the stability of graph filters under large perturbations.
problem Stability of graph filters under large edge rewires.
method Proves a bound on stability using frequency response and community structure.
result Graph filter stability depends on perturbation to community structure.
Deep GNNs and self-supervision boost graph learning at scale.
problem Efficiently deploying GNNs at large scale remains challenging.
method Two large-scale GNNs: a deep transductive node classifier and a very deep inductive graph regressor.
result Award-level performance on MAG240M and PCQM4M benchmarks.
Augments graph node features to improve GNN performance.
problem Improving graph neural networks' performance on large-scale datasets.
method Iteratively augments node features with gradient-based adversarial perturbations.
result Boosts model performance in node classification, link prediction, and graph classification tasks.
A new GCN variant tackles large eigengaps in dense graphs and hypergraphs.
problem Large eigengaps in dense graphs and hypergraphs hinder popular GCN architectures.
method Uses pseudoinverse of the Laplacian and low-rank approximation for efficient computation.
result Improves runtime and accuracy in various experiments with real-world datasets.
GraphSAGE generates node embeddings for unseen data in large graphs.
problem Inductive learning of node embeddings for unseen nodes in large graphs.
method Generative function that aggregates sampled node features from local neighborhoods.
result Outperforms baselines on inductive node classification tasks.
Optimizes graph spectral density learning for large networks.
problem Ad-hoc kernel function and bandwidth selection in graph spectral techniques.
method Maximum Entropy approach to learn a smooth graph spectral density.
result Outperforms comparable iterative spectral approaches on synthetic and real graphs.
Method finds multiple noisy graph templates in large graphs.
problem Finding multiple graph templates in noisy large graphs.
method Iteratively penalizes node-pair similarity matrix in matched filter algorithm.
result Method can sequentially discover multiple templates under mild model conditions.
A scalable method to learn causal graphs from large data.
problem Learning causal graphs from large scale data is challenging.
method Differentiable Adjacency Test (DAT) to evaluate adjacency in causal graphs.
result DAT-Graph can learn graphs of 1000 variables with state-of-the-art accuracy.
A fast graph embedding method for large graphs.
problem Efficiently embedding large graphs for various applications.
method One-hot graph encoder embedding with linear complexity.
result Graph encoder embedding is approximately normally distributed and converges to its mean.
StruClus clusters large graph datasets efficiently and interpretably.
problem Clustering large-scale graph databases efficiently and interpretably.
method Frequent subgraph sampling, projection-based clustering, parallelization.
result StruClus achieves high quality clusterings with linear runtime growth and interpretability.
FastGAT reduces GNN computation time by 10x using graph sparsification.
problem High computational burden in attention-based GNNs.
method Spectral sparsification to generate optimal graph pruning.
result Per-epoch time is almost linear in graph nodes, reducing computational time by up to 10x.
Graph signal processing detects hallucinations in large language models.
problem Detecting factual reasoning from hallucinations in large language models.
method Modeling transformer layers as dynamic graphs, using spectral analysis to define diagnostics.
result Spectral signatures can distinguish different types of hallucinations and achieve high accuracy.
Geometric approach monitors dynamic large graphs, detects major events.
problem Monitoring dynamic large graphs is challenging due to local changes affecting global properties.
method Developed a geometric approach using Ollivier-Ricci curvature for real-time monitoring.
result Detects major events and changes via graph embedding geometry.
Ripple Walk Training: A Subgraph-based training framework for Large and Deep Graph Neural Networkcs.LG
Ripple Walk Training tackles graph neural network training issues for large and deep graphs.
problem Neighbors explosion, node dependence, and oversmoothing in large and deep GNNs.
method Subgraph-based training framework with Ripple Walk Sampler for high-quality subgraph sampling.
result RWT improves training efficiency and reduces space complexity for deep and large GNNs.