SIG-VAE enhances VGAE for graph data modeling.
problem Limited flexibility in VGAE for graph data.
method Hierarchical variational framework with Bernoulli-Poisson link decoder.
result SIG-VAE outperforms state-of-the-art methods on graph tasks.
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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.
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48 results for “graph data modeling”
Proposes Continuous Graph Flow for modeling graph data.
problem Modeling complex distributions of graph-structured data.
method Generative continuous flow based on ordinary differential equations.
result Significantly better performance on diverse generation tasks.
Adaptive graph auto-encoder improves general data clustering.
problem Extending graph convolution networks to general clustering tasks.
method Adaptive graph construction based on generative perspective, novel decoder design.
result Model performs well in weighted graph scenarios.
Enhances graph classification models on small datasets.
problem Over-fitting and undergeneralization on small-scale benchmark datasets.
method Data augmentation via graph structure transformation and model evolution framework.
result Average improvement of 3 - 13% accuracy on graph classification tasks.
Graph data sets often contain isomorphism bias, artificially inflating model performance.
problem Isomorphism bias in graph data sets causing inflated model performance.
method Analysis of 54 graph data sets, recommendations for model setup, open sourcing new data sets.
result Graph data sets commonly contain isomorphism bias, artificially inflating model performance.
Graph Beta Diffusion (GBD) generates graphs with mixed discrete and continuous components.
problem Generating graphs with mixed discrete and continuous components.
method Introduces Graph Beta Diffusion (GBD) using a beta diffusion process.
result Competes strongly with existing models across graph benchmarks.
A new GNM model outperforms MLP for tabular data.
problem Learning with tabular data.
method Proposes Graph Neural Machine (GNM) replacing MLP's graph representation with a nearly complete graph and using synchronous message passing.
result GNM outperforms MLP in classification and regression tasks.
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.
Graph neural networks improve financial modeling of complex data.
problem Complex financial data and market volatility.
method Review and categorize GNN models for financial graphs.
result GNN models enhance performance in financial tasks.
Combines curvature descriptors with TDA for graph model evaluation.
problem Evaluating graph generative models efficiently and accurately.
method Combines graph curvature descriptors with topological data analysis.
result Robust, expressive descriptors for graph generative models.
Proposes a model combining graph networks and variational Bayes for graph data.
problem Probabilistic modeling of graph structured data.
method Combines graph networks and variational Bayes for probabilistic modeling of graph data.
result Demonstrates effectiveness on wind farm monitoring and Gaussian Process data.
A guide to using low-pass graph filters for network data.
problem Understanding and processing graph data with low-pass filters.
method Definition and application of low-pass graph filters to graph data.
result Low-pass filters effectively retain lower frequency graph data contents.
New graph convolution captures local features on non-Euclidean grids.
problem Capturing local features on irregular, coarse non-Euclidean grids.
method Low-rank learnable local filters in graph convolutions.
result Proves more expressive than previous spectral graph convolution methods.
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods. We release a new data set, compiled from International Planning Competitions (IPC), for benchmarking graph classification, regression, and related tasks. Apart from the graph construction (based on AI planning…
Graph-Dictionary model for sparse multivariate signal representation.
problem Capturing complex relational information in multivariate signals.
method Graph dictionaries and bilinear primal-dual splitting algorithm.
result Graph-dictionary model outperforms baselines in signal reconstruction and classification.
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.
Unified framework models graph data as a mixture of graphons using graph moments.
problem Graph datasets often mix from multiple underlying distributions.
method Model graph data as a mixture of graphons, using graph moments to cluster graphs.
result Graphs from similar graphons exhibit similar motif densities, enabling principled estimation of graphon mixture components.
TGNN4I model forecasts irregularly observed graph data using ODEs.
problem Forecasting graph-structured data with irregular time steps and partial observations.
method Introduces a time-continuous latent state in each node using ODEs and GRUs, integrating graph neural network layers.
result Validated usefulness of graph structure and time-continuous dynamics in irregular observation settings.
G5 universal GRAPH-BERT learns graph representations across different datasets.
problem Learning graph representations across diverse graph datasets with distinct input and output configurations.
method G5 introduces a pluggable model architecture with input and output components for each graph data source, connected via a unified layer and fusion layer.
result G5 removes obstacles for cross-graph representation learning and transfer, even for sparse data.
Generative Adversarial Graph Neural Network (Sig-Graph GAN) models financial time series data.
problem Challenges in generating synthetic data for non-stationary financial time series.
method Integrates time-series signature, LSTM, and GNNs with visibility graph algorithm.
result Sig-Graph GAN outperforms baseline methods in replicating time series data distributions.
Graph Mixture Density Networks model multimodal data on graphs.
problem Challenging conditional density estimation problems with structured data.
method Combining mixture models and graph representation learning.
result Significant improvement in likelihood of epidemic outcomes.
Graph learning methods have recently been receiving increasing interest as means to infer structure in datasets. Most of the recent approaches focus on different relationships between a graph and data sample distributions, mostly in settings where all available data relate to the same graph. This is, however, not alway…
G1 uses RL to enhance LLMs' graph reasoning, improving performance on diverse tasks.
problem Limited graph reasoning abilities of LLMs, especially in synthetic graph-theoretic tasks.
method Curated synthetic graph dataset, RL training on LLMs.
result Significant improvements in graph reasoning, zero-shot generalization to unseen tasks.
As a new approach to train generative models, \emph{generative adversarial networks} (GANs) have achieved considerable success in image generation. This framework has also recently been applied to data with graph structures. We propose labeled-graph generative adversarial networks (LGGAN) to train deep generative model…
Graph transformation framework improves graph neural network performance.
problem Data quality issues in graph neural networks.
method Test-time graph transformation framework (GTrans).
result Significant performance improvements (up to 3.8%) across various datasets.
Generative model for creating graphs with new communities.
problem Generating graphs with a new community structure.
method Fit Gaussian mixture model to latent space data and add new clusters based on MDL principle.
result Empirically demonstrated effectiveness of GCA for generating graphs with new community structures.
Graph deep learning models, such as graph convolutional networks (GCN) achieve remarkable performance for tasks on graph data. Similar to other types of deep models, graph deep learning models often suffer from adversarial attacks. However, compared with non-graph data, the discrete features, graph connections and diff…
iGNN tackles inverse graph prediction using invertible neural networks.
problem Inverse graph prediction problem in data analysis and machine learning.
method Developed invertible graph neural network (iGNN) to solve inverse prediction problem on graphs.
result iGNN model allows efficient generation from output labels and forward prediction.
Graph-based methods have been demonstrated as one of the most effective approaches for semi-supervised learning, as they can exploit the connectivity patterns between labeled and unlabeled data samples to improve learning performance. However, existing graph-based methods either are limited in their ability to jointly …
A new model learns graph structures from data.
problem Learning graph topologies from data.
method Proposes a learning to optimise (L2O) approach to learn graph structures from node data.
result The proposed model learns graph structures more efficiently than classic iterative algorithms.
Graphs are fundamental data structures which concisely capture the relational structure in many important real-world domains, such as knowledge graphs, physical and social interactions, language, and chemistry. Here we introduce a powerful new approach for learning generative models over graphs, which can capture both …
Graph Unlearningcs.LG
GraphEraser improves unlearning efficiency for graph data.
problem Improving unlearning efficiency for graph data.
method Two novel graph partition algorithms and a learning-based aggregation method.
result Achieves up to 35.94× unlearning time improvement on large datasets.
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.
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.
Bayesian graph learning improves graph representation accuracy.
problem Inaccurate graph construction from noisy data.
method Non-parametric Bayesian graph model for posterior inference of graph adjacency matrices.
result Model scales well to large graphs and improves node classification, link prediction, and recommendation tasks.
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.
This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in v…
Reconstructs graph structure from noisy data samples.
problem Efficiently discover and model structures in high-dimensional data.
method Combining topological data analysis with numerical modelling.
result Recovery of graph structure from noisy point cloud samples.
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.
Graph representation converts complex networks into vectors for easier analysis.
problem Efficient analysis of large network data.
method Introduces graph representation and network embedding models.
result Efficiently converts graph data into low-dimensional vectors.
Paper introduces a novel point process model for graph data using GNNs.
problem Modeling discrete event data over graphs with influence kernel.
method Combines Hawkes kernel and Graph Neural Networks (GNN) for event prediction.
result Achieves superior predictive performance compared to state-of-the-art.
Graph learning from data represents a canonical problem that has received substantial attention in the literature. However, insufficient work has been done in incorporating prior structural knowledge onto the learning of underlying graphical models from data. Learning a graph with a specific structure is essential for …
Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exis…
New method uses graph generative models for graph classification.
problem Graph classification for non-relational i.i.d. data.
method Derive classification formulas from GGM, train generative graph auto-encoder model.
result New conditional ELBO for training graph auto-encoder model.
Graph neural networks tackle representation learning for small and giant graphs.
problem Learning representations from small and giant graphs.
method Various graph neural network models tailored for small and giant graphs.
result Graph neural networks achieve state-of-the-art performance on node and graph classification tasks.
Graph data augmentation improves GNN performance in node classification.
problem Improving generalizability of graph neural networks (GNNs) in semi-supervised node classification.
method Introduces GAug framework for graph data augmentation using neural edge predictors.
result GAug framework improves GNN-based node classification performance across various architectures and datasets.
Paper presents voxel graph operators for vector data models.
problem Efficient conversion and analysis of geometric models.
method Topological voxelization, graph construction, differential operator derivation.
result Discrete differential and integral operators from voxel complexes.
Graphs model human mobility patterns, reducing errors in data matching.
problem Lack of high-quality data and computational resources for graph-based mobility analysis.
method Embedding graphs into a continuous space to address matching, modeling, and visualization challenges.
result Approx 40% decrease in error on average in matched graphs vs unmatched ones.