Develops a private synthetic graph generator using Gromov-Wasserstein distance.
problem Creating private synthetic networks for complex data.
method Random connection model, fused Gromov-Wasserstein distance, differential privacy.
result Effective algorithm for generating private synthetic graphs with theoretical guarantees.
We use multiple measures of graph complexity to evaluate the realism of synthetically-generated networks of human activity, in comparison with several stylized network models as well as a collection of empirical networks from the literature. The synthetic networks are generated by integrating data about human populatio…
Transformer model pretrains on synthetic graphs for AD detection.
problem Limited labeled data and class imbalance in AD diagnosis.
method Diffusion-generated synthetic graphs, Graph Transformers, transfer learning.
result Framework outperforms baselines in AD diagnosis metrics.
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.
Study shows neural ODEs generalize well on synthetic graphs but struggle with degree heterogeneity and clustering.
problem Understanding neural ODEs on complex networks, especially with varying graph sizes and structures.
method Synthetic data from five dynamical systems on graphs, using Barabási-Barzel form vector fields.
result Degree heterogeneity and dynamical system type are primary factors affecting neural ODEs' generalization.
Neural network learns causal graph structure from data.
problem Inferring causal graph structure from observational and interventional data.
method Supervised training of a neural network on synthetic graphs.
result Learned model generalizes to new graphs, robust to distribution shifts, and outperforms existing methods.
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.
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.
To the best of our knowledge, this paper presents the first large-scale study that tests whether network categories (e.g., social networks vs. web graphs) are distinguishable from one another (using both categories of real-world networks and synthetic graphs). A classification accuracy of 94.2% was achieved using a …
A model learns causal graphs from summary statistics of synthetic data.
problem Causal discovery algorithms are brittle with large sets of variables and limited data.
method A supervised model trained on synthetic data predicts causal graphs from summary statistics.
result The model generalizes well beyond its training set and runs on large graphs.
This paper proposes an autoregressive (AR) model for sequences of graphs, which generalises traditional AR models. A first novelty consists in formalising the AR model for a very general family of graphs, characterised by a variable topology, and attributes associated with nodes and edges. A graph neural network (GNN) …
AgraSSt assesses graph generators using Stein operators and kernel discrepancies.
problem Assessing the quality of graph generators that are implicit or not in explicit form.
method AgraSSt uses Stein operators and kernel discrepancies to assess graph generators, providing interpretable criticisms.
result Theoretical guarantees and empirical validation for various graph models.
Graph matching involves combinatorial optimization based on edge-to-edge affinity matrix, which can be generally formulated as Lawler's Quadratic Assignment Problem (QAP). This paper presents a QAP network directly learning with the affinity matrix (equivalently the association graph) whereby the matching problem is tr…
How can we model networks with a mathematically tractable model that allows for rigorous analysis of network properties? Networks exhibit a long list of surprising properties: heavy tails for the degree distribution; small diameters; and densification and shrinking diameters over time. Most present network models eithe…
Generative model captures hubs and dense communities in social networks.
problem Capturing both hubs and dense communities in social networks.
method Graphon mixture model with a new condition on sparse graphs.
result Estimation of hub normalized degree and graphon for sparse components.
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.
We perform theoretical and algorithmic studies for the problem of clustering and semi-supervised classification on graphs with both pairwise relational information and single-point feature information, upon a joint stochastic block model for generating synthetic graphs with both edges and node features. Asymptotically …
Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite, an algorithmic framework for unsupervised learn…
SPECTRE uses spectral conditioning to generate larger graphs without mode collapse.
problem Overcoming expressivity and mode collapse in one-shot graph generators.
method SPECTRE generates graph Laplacian spectrum and eigenvectors to model graph structure.
result SPECTRE outperforms state-of-the-art deep autoregressive generators in fidelity and speed.
Graph neural networks speed up nonnegative matrix factorization.
problem Efficiently factorize nonnegative matrices for various applications.
method Developed a graph neural network that combines bipartite self-attention with ADMM updates.
result Significant acceleration achieved in nonnegative matrix factorization.
A new graph generator uses heat diffusion on graph Laplacians to create new graph structures.
problem Creating realistic and diverse graph structures for various applications.
method Adapting the Generator Matching paradigm to graph data, using graph Laplacian and heat kernel for diffusion.
result The method effectively generates graphs with structural properties of real and synthetic graphs.
New algorithm minimizes FE objectives for synthetic AIF agents.
problem Minimizing Free Energy for synthetic active inference agents.
method Variational message passing on Constrained FFGs.
result Message passing algorithms induce epistemic behavior in AIF agents.
Fixed-parameter tractability of private synthetic data generation
problem Generating synthetic data under differential privacy
method Linear programming and subsampled private multiplicative weights method
result Optimal error rates across all regimes
GraphBSI generates graphs by refining a belief in continuous space, outperforming existing models.
problem Generating discrete, unordered graph data is challenging for traditional models.
method GraphBSI uses Bayesian Sample Inference (BSI) to iteratively refine a belief over graph distribution parameters.
result GraphBSI outperforms existing one-shot graph generative models on molecular and synthetic graph generation benchmarks.
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 …
A novel model-selection method for dynamic networks using synthetic data.
problem Classifying and understanding the growth mechanisms of dynamic networks.
method Training a classifier on synthetic network data generated by nine random graph models, using dynamic features that count new links.
result Achieves near-perfect classification of synthetic networks, outperforming state-of-the-art methods.
AutoGraph uses transformers to efficiently generate graphs as sequences.
problem Efficiently generating large, sparse graphs without expensive node features.
method Flattening graphs into sequences and using decoder-only transformers.
result AutoGraph achieves state-of-the-art performance on synthetic and molecular benchmarks.
Graphs are ubiquitous real-world data structures, and generative models that approximate distributions over graphs and derive new samples from them have significant importance. Among the known challenges in graph generation tasks, scalability handling of large graphs and datasets is one of the most important for practi…
This paper evaluates metrics for graph generative models, addressing common pitfalls.
problem Evaluating and comparing graph generative models effectively.
method Systematic evaluation of MMD, analysis of synthetic and real graphs, practical recommendations.
result MMD can be problematic; practical solutions are provided.
Inspired by the tremendous success of deep generative models on generating continuous data like image and audio, in the most recent year, few deep graph generative models have been proposed to generate discrete data such as graphs. They are typically unconditioned generative models which has no control on modes of the …
SIGL learns scalable graphons from graphs.
problem Estimating graphons from graphs of varying sizes.
method Combines INRs and GNNs for scalable graphon estimation.
result SIGL learns consistent graphons at arbitrary resolutions.
Study identifies and analyzes spurious correlations in data-driven models.
problem Spurious correlations in data-driven models are unreliable and hard to detect.
method Collect and analyze synthetic datasets generated from causal graphs to investigate spurious correlations.
result Patterns connecting spurious correlation hypotheses and model design choices were observed.
XGNN explains graph neural networks by generating graphs that maximize model predictions.
problem Lack of explainable models for graph neural networks.
method Train a graph generator to maximize model predictions, using reinforcement learning and graph rules.
result Generated graphs provide insights into how GNNs work and can guide model improvement.
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.
Adaptive GPR-GNN optimizes node feature and topology learning.
problem Optimizing GNNs for both node features and graph topology, regardless of homophily or heterophily.
method Adaptive Universal Generalized PageRank (GPR) Graph Neural Network (GPR-GNN) that learns optimal GPR weights.
result Significant performance improvement on node classification tasks compared to state-of-the-art GNNs.
Most of the successful deep neural network architectures are structured, often consisting of elements like convolutional neural networks and gated recurrent neural networks. Recently, graph neural networks have been successfully applied to graph structured data such as point cloud and molecular data. These networks oft…
Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups co…
Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as …
Localized signal representation on graph bundles using Fourier analysis.
problem Representing signals on graph bundles with twists.
method Partition of unity and product factorization over the base graph.
result Lifted bases for signal spaces of graph bundle components.
The paper improves GNN generalization theory by considering graph manifolds.
problem Improper GNN generalization bounds ignoring graph structures.
method Taking a manifold perspective, the paper establishes GNN generalization theory.
result GNN generalization bounds decrease linearly with graph size and spectral continuity.
This work studies the denoising of piecewise smooth graph signals that exhibit inhomogeneous levels of smoothness over a graph, where the value at each node can be vector-valued. We extend the graph trend filtering framework to denoising vector-valued graph signals with a family of non-convex regularizers, which exhibi…
Mixes higher-order simplicial complexes for data augmentation.
problem Lack of labeled data for complex systems with multiway interactions.
method Proposes mixup mechanisms for simplicial complexes, including linear and nonlinear mixup, and a convex clustering mixup.
result Synthetic simplicial complexes interpolate between existing data based on homomorphism densities.
Convex optimization method infers latent structure in random dot product graphs.
problem Inferring latent probability matrix of random dot product graphs.
method Conic programming with nuclear norm regularization.
result Asymptotic consistency of probability estimates and recovery of latent structure.
Bayesian VAR model discovers Granger causality with uncertainty-aware binary graphs.
problem Discovering Granger causal relations from multivariate time-series data.
method Bayesian Vector AutoRegression with factorised Granger-Causal Graphs.
result Our method achieves better performance, especially in low-data regimes.
This work improves GNN training efficiency by maximizing ego-graph information.
problem Training dedicated GNNs is costly for large-scale graphs.
method Proposes EGI (Ego-Graph Information maximization) to capture essential graph information and establish a theoretical framework for transfer learning.
result Demonstrates the effectiveness of EGI in improving GNN training efficiency and transferability.
A method identifies domain-general features using causal graph constraints and regularization.
problem Identifying domain-general features without prior knowledge of spurious features.
method Proposes a novel regularization framework based on causal graph constraints.
result Demonstrates effectiveness in both synthetic and real-world data, outperforming state-of-the-art methods.
Enhances GNNs for causal relationship learning.
problem Lack of robust causal modeling in GNNs.
method Synthesized dataset with known causal relationships, lightweight GNN module.
result Empirically validated GNN module improves causal learning.
CausalTime generates realistic time-series for TSCD evaluation.
problem Lack of realistic synthetic datasets for TSCD performance evaluation.
method Harnessing deep neural networks and normalizing flow for dynamics, extracting causal graphs, and deriving ground truth causal graphs.
result Generated datasets accurately reflect real data and ground truth causal graphs.