Improved spectral-based GCN for directed graphs.
problem Cannot directly work on directed graphs.
method Redefined Laplacians to improve propagation model.
result Outperforms state-of-the-art methods on directed graph datasets.
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.
169,181 papers · 148 categories
Trend · papers per month
48 results for “Graph Bases”
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.
Paper develops a method to identify graphs and filters from filtered signals.
problem Learning graphs and filters from filtered signals.
method Developed an algorithm to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations.
result The proposed algorithm outperforms current state-of-the-art methods.
Graph Cascades rewire graphs to improve structure-aware learning.
problem Improving graph neural networks and transformers for structure-aware learning.
method Graph Cascades uses contagion-based diffusion processes to construct an auxiliary graph with reinforced edges.
result Graph Cascades improves node-classification benchmarks across various graph types.
End-to-end graph-based SSL learns all graph factors dynamically.
problem Learning quality of graph in SSL is crucial but difficult.
method Proposes an end-to-end approach to optimize all graph factors.
result Demonstrates effectiveness on benchmark datasets.
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.
Flexible framework for semi-supervised learning on graphs.
problem Predicting unlabeled graph data using limited labeled data.
method Generative framework leveraging features, graph structure, and labels.
result Outperforms state-of-the-art models in most settings.
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.
Efficient memory layer improves graph neural networks for graph classification and regression.
problem Efficiently learning node representations and graph coarsening for arbitrary graph topology.
method Introduces a memory layer for GNNs that learns node representations and graph coarsening, and two new networks: MemGNN and GMN.
result Proposed models achieve state-of-the-art results in graph classification and regression benchmarks.
The study proves sampling-based GNNs can approximate training on full graphs with small subgraphs.
problem Training Graph Neural Networks (GNNs) on large graphs is computationally expensive.
method Theoretical framework using graph local limits to prove approximation of GNN training on small samples.
result Parameters learned from sampling-based GNNs on small subgraphs are close to those on full graphs.
PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.
problem Efficiently compute graph similarity scores for large graphs.
method Graph partitioning followed by subgraph-level and node-level comparisons using a graph neural network.
result PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks.
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.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
Few-shot graph classification on graphs with limited labeled examples.
problem Limited labeled data for graph classification.
method Graph spectral measures to cluster graphs into super-classes, then use GNNs.
result Improved classification performance on few-shot graph classification tasks.
A new method for fast graph embedding using diffusion graphs.
problem Efficiently generating graph embeddings for large networks.
method Diffusion graphs for rapid vertex sequence generation.
result Improved accuracy and performance with higher edge density.
A new flow-based framework improves graph-based semi-supervised learning while enhancing interpretability.
problem Improving interpretability of semi-supervised learning on graphs.
method Introduces a flow-based learning framework that subsumes and enhances Laplacian-based approaches.
result The flow-based framework improves prediction accuracy without sacrificing interpretability.
Sparse hierarchical graph classification improves graph-based benchmarks.
problem Sparse hierarchical graph classification challenges.
method Combining recent advances in graph neural network design, differentiable graph coarsening, and sparse pooling.
result Competitive hierarchical graph classification results possible without sacrificing sparsity.
Proposes a method to preserve graph similarities for better clustering accuracy.
problem Sub-optimal performance due to non-similarity-preserving kernels in graph-based clustering.
method Adaptive graph learning method that preserves pairwise similarities and unifies clustering and graph learning.
result Improves clustering accuracy by preserving pairwise similarities in the graph.
Two CSSL-based methods improve graph classification with limited labeled data.
problem Limited labeled data for graph classification leads to overfitting.
method Contrastive self-supervised learning (CSSL) for graph encoders pretraining and regularization.
result CSSL methods reduce overfitting and improve graph classification accuracy.
A new layer learns abstract relations from graph structure using finite-state automata.
problem Learning abstract relations from graph structure for program analysis.
method Relaxing the problem into learning finite-state automata policies on a graph-based POMDP and training these policies using implicit differentiation.
result GFSA layer finds shortcuts in grid-world graphs and reproduces simple static analyses on Python programs.
IDGL learns better graph structure and embeddings iteratively.
problem Improving graph neural network node embeddings and graph structure.
method Iterative Deep Graph Learning framework that dynamically stops when graph structure optimizes for downstream tasks.
result IDGL consistently outperforms state-of-the-art baselines on nine benchmarks.
GRAPH-BERT uses only attention for graph representation learning.
problem Graph neural networks over-rely on graph links and suffer from performance issues.
method GRAPH-BERT uses only attention mechanism without graph convolution or aggregation, trained on sampled subgraphs.
result GRAPH-BERT outperforms existing GNNs in learning effectiveness and efficiency.
Transformer adapts to graphs with adaptive attention and auto-regressive decoding.
problem Transformers struggle with graph data due to non-sequential nature.
method Proposes GRAT, a Transformer variant with adaptive attention and auto-regressive decoding.
result GRAT achieves state-of-the-art performance on molecule property predictions and generation tasks.
Graph construction is a crucial step in spectral clustering (SC) and graph-based semi-supervised learning (SSL). Spectral methods applied on standard graphs such as full-RBF, -graphs and -NN graphs can lead to poor performance in the presence of proximal and unbalanced data. This is because spectral methods based…
Tensor-based embeddings improve knowledge graph fact prediction.
problem Predicting new facts in knowledge graphs.
method Knowledge-Enriched Tensor Factorization
result 5% to 50% relative improvement over state-of-the-art techniques.
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representations necessarily emphasize particular aspects of the molecular structure while ignoring others, rather than allowing the model to make data-d…
Study explains Graph Convolutional Networks decisions.
problem Difficulty in understanding Graph Network decisions.
method Gradient-based and decomposition-based techniques.
result Sets groundwork for future explainability development.
Paper presents an optimization-based attack and defense for graph neural networks.
problem Adversarial robustness of graph neural networks (GNNs).
method Gradient-based attack and optimization-based adversarial training.
result Optimization-based attack can significantly decrease GNN classification performance with minimal edge perturbations.
Proposes a method to adapt labels on graphs with few labeled nodes.
problem Domain adaptation for graphs with limited labeled nodes.
method Optimization problem solving label transfer using spectral graph wavelets.
result Method yields satisfactory classification accuracy compared to existing methods.
Method to create Heegaard splittings for graph manifolds.
problem Constructing Heegaard splittings of graph manifolds.
method Method to construct Heegaard splittings of oriented graph manifolds with orientable bases.
result A method to create Heegaard splittings of oriented graph manifolds.
PathBoost boosts graph-level predictions using path-based features.
problem Graph-level classification and regression challenges.
method Gradient tree boosting method for graph-level prediction.
result PathBoost outperforms graph neural networks and graph kernel approaches in many cases.
A new flow-based model for molecular graphs achieves better performance with fewer parameters.
problem Generating molecular graphs efficiently and accurately.
method Graph residual flow (GRF) based on residual flows for molecular graphs, with invertibility conditions derived.
result The GRF model achieves comparable performance to existing models with significantly fewer parameters.
A new graph neural network tackles oversmoothing and generalization issues.
problem Oversmoothing and poor generalization for unseen graphs in graph neural networks.
method Graph Entities with Step Mixture via random walk (GESM) that considers both edge-based and node-based features.
result GESM achieves state-of-the-art or comparable performances on benchmark datasets.
Graphs can be fooled by small edge changes, but this work protects them.
problem Adversaries can manipulate graph data to mislead graph classification models.
method We introduce a smoothed graph classification model with a robustness guarantee.
result The smoothed model maintains consistent predictions under small adversarial perturbations.
Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
Paper optimizes graph neural networks for better structural graph classification.
problem Improving graph neural networks for structural graph classification.
method Focus on aggregation functions, specifically sum and histogram-based functions, to enhance discrimination.
result Design of a graph neural network that learns discriminative graph representations.
Graph classification improved using spectral features and wavelet filters.
problem Categorizing graphs based on their structure and node attributes.
method Derived spectral features from graph signal processing, designed two Gaussian process models: one simple and one sophisticated.
result Simple and sophisticated Gaussian process models yield competitive performance, including well-calibrated uncertainty estimates.
Paper presents a graph-based semi-supervised method for hyperspectral image classification.
problem Hyperspectral image classification with limited labeled data.
method Novel superpixel algorithm based on spectral covariance matrix, followed by superpixel graph construction and classification.
result The method outperforms state-of-the-art approaches, especially in scenarios with minimal labeled data.
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.
DMGNN predicts 3D human motions using adaptive multiscale graphs.
problem Predicting 3D skeleton-based human motions accurately.
method Dynamic multiscale graph neural networks (DMGNN) with adaptive multiscale graphs and MGCU.
result DMGNN outperforms state-of-the-art methods in short and long-term predictions.
Develops BASGCN for graph classification with improved feature learning.
problem Graph classification with information loss and imprecise representation.
method Transforms graphs into grid structures and defines a new spatial graph convolution operation.
result Reduces information loss and improves feature representation compared to existing models.
TD-GEN generates graphs using tree decomposition, improving efficiency and performance.
problem Efficiently generating graphs with statistical properties.
method Tree decomposition, permutation invariant tree generation, incremental graph generation.
result Improved graph generation efficiency and performance.
A new method learns graph distributions invariant to node ordering.
problem Graphs are hard to model due to node ordering invariance issues.
method Score-based generative modeling with permutation equivariant graph neural network.
result The method achieves better or comparable graph generation results.
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.
TGR rewires temporal graphs to improve TGNN performance.
problem Temporal graphs in evolving networks can suffer from under-reaching and over-squashing issues.
method TGR uses expander graph propagation to create message-passing highways between temporally distant nodes.
result TGR achieves state-of-the-art results on temporal graph benchmarks.
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matchingcs.LG
GraphSim computes graph similarity by matching node embeddings, outperforming existing methods.
problem Efficiently computing graph similarity between graphs of varying sizes and structures.
method GraphSim directly matches sets of node embeddings without fixed-dimensional graph representations.
result GraphSim achieves state-of-the-art performance on multiple real-world datasets.
Graph-based feedback improves bandit algorithms' performance.
problem Stochastic multi-armed bandit problem with graph feedback.
method Analysis of Thompson Sampling and UCB algorithms in graph-based feedback setting.
result Regret bounds that combine graph structure and arm means gaps.
A framework for federated graph classification over non-IID graphs.
problem Training graph mining models collaboratively across different domains with non-IID graphs.
method Graph Clusters Federated Learning (GCFL) framework, dynamically finding clusters based on GNN gradients, and a gradient sequence-based clustering mechanism (GCFL+).
result Demonstrated effectiveness of GCFL+ in reducing structure and feature heterogeneity among graphs.