Survey on learning with graph-dependent data, deriving new generalization bounds.
problem Traditional i.i.d. data assumption fails in many real-life applications.
method Collect and analyze graph-dependent concentration bounds, derive generalization bounds.
result New generalization bounds for graph-dependent data.
New inequalities for learning from graph-dependent data with stability bounds.
problem Learning from dependent data with graph dependency.
method Proved McDiarmid-type concentration inequalities for graph-dependent variables, showed concentration relies on forest complexity.
result Proved stability bounds for learning from graph-dependent data.
Forecaster uses graph Transformers to forecast spatial and time-dependent data.
problem Complex spatial and temporal dependencies in data.
method Graph Transformer architecture with sparsification for spatial and temporal dependencies.
result Forecaster significantly outperforms state-of-the-art baselines in taxi demand forecasting.
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.
PINE embeds graph nodes flexibly, capturing any neighbor dependency.
problem Learning flexible node representations from graph neighborhoods.
method PINE uses partial permutation invariant set functions to capture any possible neighbor dependencies.
result PINE outperforms state-of-the-art methods on various graph learning tasks.
BeGIN benchmarks GNNs for instance-dependent label noise in graphs.
problem Instance-dependent label noise in graph data.
method BeGIN introduces a benchmark with various noise types and evaluates noise-handling strategies across GNN architectures.
result Challenges of instance-dependent noise, especially LLM-based corruption, and the importance of node-specific parameterization.
Improved GNN handles long-range dependencies in multi-relational graphs.
problem Vanishing gradients in GNNs for multi-relational graphs.
method Proposes a Gated Graph Neural Network with improved long-range dependency handling.
result Outperforms popular GNN models in synthetic tasks.
Graph-dependent implicit regularisation improves Distributed SGD for convex problems.
problem Improving convergence rates in distributed stochastic subgradient descent.
method Graph-dependent implicit regularisation strategies for Distributed SGD.
result Established statistical learning rates retaining centralised guarantees.
Graph WaveNet models spatial-temporal graphs by learning hidden dependencies and long sequences.
problem Capturing hidden spatial dependencies and long-range temporal sequences in graphs.
method Graph WaveNet integrates adaptive dependency matrix learning and stacked dilated 1D convolution.
result Graph WaveNet outperforms existing methods on public traffic network datasets.
FGNN generalizes graph neural networks to capture higher-order dependencies.
problem Capturing higher-order dependencies in graph-structured data.
method Introducing a factor graph neural network (FGNN) that can represent Max-Product Belief Propagation.
result FGNN effectively represents Max-Product Belief Propagation and performs well on both synthetic and real datasets.
Novel graph neural network combines random walks with local message passing.
problem Graph neural networks struggle with long-range dependencies.
method Combines random walks with local message passing in a novel architecture.
result Significant performance improvements on graph benchmarks.
IGNN captures long-range graph dependencies using fixed-point equations.
problem Limited GNN ability to capture long-range graph dependencies.
method Fixed-point equilibrium equations involving implicitly defined state vectors, leveraging Perron-Frobenius theory and projected gradient descent.
result IGNN consistently captures long-range dependencies and outperforms state-of-the-art GNNs.
PAGTN improves molecular property prediction by leveraging longer-range graph dependencies.
problem Local aggregation in GCNs misses higher-order graph properties.
method PAGTN uses path features and global attention layers to capture longer-range dependencies.
result PAGTN outperforms GCNs on various molecular property prediction datasets.
Proposes TDNs for learning complex video structures.
problem Complex temporal dependencies in sequential data, especially videos.
method Temporal Dependency Networks (TDNs) using graph representations and graph convolutions.
result Efficiently learns complex semantic structures of video data.
Proposes a novel graph learning framework for robust graph topology learning from graph signals.
problem Graph learning for revealing node relationships in data entities.
method Functional learning with smoothness-promoting graph learning, incorporating Kronecker product kernel.
result Improves robustness against missing and incomplete information in graph signals.
BAM model learns graph structure from data with robustness across linear and non-linear dependencies.
problem Detecting dependencies in datasets for graph structure learning.
method Proposes BAM, a neural network model using structural equation models and Chebyshev polynomials for training, with bilinear attention mechanism.
result Demonstrates robust generalizability and superior performance in graph estimation.
Alt-GNNs improve travel mode choice modeling by integrating graph neural networks with GEV models.
problem Capturing alternative dependence in discrete choice models with predefined, symmetric, and uniform dependence.
method Introducing Alternative Graph Neural Networks (Alt-GNNs) that embed alternative dependence within a unified framework.
result Alt-GNNs significantly improve predictive performance over benchmark models in travel mode choice datasets.
Proposes a GNN framework for multivariate time series forecasting.
problem Lack of exploiting latent spatial dependencies in multivariate time series forecasting.
method Automatically extracts graph structures from multivariate time series data, integrates external knowledge, and uses mix-hop and dilated inception layers for capturing dependencies.
result Outperforms state-of-the-art methods on 3 out of 4 benchmark datasets.
Paper improves risk bound for MTL with graph-dependent data.
problem Sub-optimal risk bound in multi-task learning with graph-dependent data.
method Proposes a new Bennett-type inequality and develops new Talagrand-type inequality and local fractional Rademacher complexity.
result Derives a sharper risk bound of O(nlogn). Unified framework for clustering and learning causal graphs across subjects.
problem Bias and obscured subpopulation-specific dependencies in multivariate systems.
method Directed Acyclic Graph-based Dependency Clustering via Alternating Direction Method of Multipliers (DAG-DC-ADMM) integrated with Structural Equation Modeling (SEM).
result Unified framework recovers cluster-specific causal dependency structures with high true positive rate and low false discovery rate.
The covariance graph (aka bi-directed graph) of a probability distribution p is the undirected graph G where two nodes are adjacent iff their corresponding random variables are marginally dependent in p. In this paper, we present a graphical criterion for reading dependencies from G, under the assumption that $…
New method improves tensor completion for weakly-dependent spatiotemporal data.
problem Improving tensor completion for weakly-dependent data on graphs.
method Introducing L1-norm and Graph Laplacian penalties for low-rank tensor decomposition and completion. result Improved performance in metro passenger flow prediction.
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are d…
CB-GLNs learn video data's complex dependencies via graph representation.
problem Capturing complex dependency structures in sequential data like videos.
method Represent video data as a graph, find compositional dependencies via graph-cut and message passing.
result CB-GLNs efficiently learn video data's semantic compositional structure.
DynDepNet learns dynamic brain graphs from fMRI data for better prediction performance.
problem Static brain graphs from fMRI data lead to poor GNN performance.
method Dynamic Graph Structure Learning for time-varying brain connectivity.
result DynDepNet achieves state-of-the-art sex classification accuracy on real-world fMRI data.
Boost GNNs for node classification by incorporating label dependencies.
problem Current GNNs lack expressiveness and fail to capture label dependencies.
method Proposes a collective learning framework combining collective classification and self-supervised learning.
result Consistent, significant improvement in node classification accuracy across various GNNs.
Graph attention network improves MLTC by capturing label dependencies.
problem Ignoring label dependencies in MLTC tasks.
method Graph attention network model that captures label dependencies.
result The model achieves similar or better performance than state-of-the-art models.
A method for non-projective dependency parsing without fixed edge order.
problem Non-projective dependency parsing without fixed edge order.
method Incremental edge prediction, blending graph, transition, and easy-first parsing.
result Successfully parses near state-of-the-art on projective and non-projective languages.
DA-GNN improves robustness of GNNs by modeling noise dependencies.
problem Real-world graph node features often contain noise, leading to performance degradation in GNNs.
method DA-GNN captures noise dependencies using variational inference and new benchmark datasets.
result DA-GNN consistently outperforms existing baselines across various noise scenarios.
Geom-GCN improves graph neural networks by preserving structural information and capturing long-range dependencies.
problem Weaknesses in MPNNs' aggregators: loss of structural information and lack of long-range dependencies.
method Proposes a geometric aggregation scheme with three modules: node embedding, structural neighborhood, and bi-level aggregation.
result Achieved state-of-the-art performance on various graph datasets.
Framework for universal graph function approximators outperforms existing methods.
problem Graph classification and separation of graph classes.
method Inspired by persistent homology, dependency parsing, and multivalued functions, the framework constructs universal approximators on graph isomorphism classes.
result Achieves state-of-the-art performance on four graph datasets.
T-GCN predicts traffic using neural networks for spatial and temporal data.
problem Accurate real-time traffic forecasting in urban networks.
method Combines GCN for spatial and GRU for temporal data analysis.
result T-GCN outperforms state-of-the-art baselines on real-world traffic datasets.
Paper models graph edge dependencies using latent variables for community detection.
problem Graphs' edge dependencies not fully explained by community membership.
method Introduces auxiliary latent variables to model edge dependencies and analyzes conditions for exact recovery.
result Exact recovery possible by semidefinite programming down to maximum likelihood threshold.
Paper proposes methods to improve graph domain adaptation by decorrelating node features.
problem Challenges in transferring knowledge from one graph to another.
method Proposes decorrelating node features using GCN and graph transformer layers.
result Significant performance enhancements and clear visualizations of learned representations.
Gaussian graphical models are widely used to represent conditional dependence among random variables. In this paper, we propose a novel estimator for data arising from a group of Gaussian graphical models that are themselves dependent. A motivating example is that of modeling gene expression collected on multiple tissu…
Paper tackles order-dependence in structure learning of multivariate regression chain graphs.
problem Order-dependence in structure learning of multivariate regression chain graphs.
method Proposes modifications to the PC-like algorithm to remove order-dependence.
result Improved performance in high-dimensional settings with modifications to the PC-like algorithm.
Proposes continuous graph neural networks to capture long-range dependencies.
problem Capturing long-range dependencies in graph data.
method Defines continuous dynamics for graph neural networks using diffusion-based methods.
result Proposed continuous graph neural networks are effective and deeper networks can capture long-range dependencies.
Paper estimates differences in conditional independence graphs from time-dependent data.
problem Estimating changes in conditional dependencies between two time series with known similar structure.
method Penalized D-trace loss function approach in the frequency domain, using Wirtinger calculus, with convex and non-convex penalties.
result Established sufficient conditions for consistency and graph recovery in high-dimensional settings.
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph clustering algorithm to find a suitable partition of the graph. Our main question is if and how the construction of the graph (…
MRA-BGCN improves traffic forecasting accuracy through complex graph interactions.
problem Challenging traffic forecasting due to spatial-temporal dependency and uncertainty.
method Proposes MRA-BGCN, a deep learning model that uses bicomponent graph convolution and multi-range attention.
result MRA-BGCN achieves state-of-the-art results on real-world traffic datasets.
Improves causal graph learning on dependent binary data.
problem Challenges in learning causal graphical models from dependent binary data.
method Decorrelation-based approach using latent utility model and EM-like algorithm.
result Significant improvement in accuracy of causal graph learning.
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.
Estimates marginal independence structure of Bayesian networks from data.
problem Learning the marginal independence structure of Bayesian networks from observational data.
method Using Gröbner basis and MCMC method (GrUES) to connect and recover the true structure.
result GrUES recovers the true marginal independence structure at a higher rate than simple independence tests.
Chordal graphs can be used to encode dependency models that are representable by both directed acyclic and undirected graphs. This paper discusses a very simple and efficient algorithm to learn the chordal structure of a probabilistic model from data. The algorithm is a greedy hill-climbing search algorithm that uses t…
Graph Neural Networks (GNNs) model graph data for tasks like physics, molecular, and disease prediction.
problem Modeling graph data for various learning tasks.
method Message passing between graph nodes using GNN variants like GCN, GAT, GRN.
result GNNs have achieved breakthrough performance in deep learning tasks.
We consider the Qk flow on complete non-compact graphs. We prove that a complete graph evolves by the Qk curvature up to some time T depending on the radius of a sphere enclosed by the initial graph.
This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.
problem Designing causal bandit algorithms with unknown causal graphs and stochastic intervention models.
method Establishes novel regret bounds and presents a computationally efficient algorithm for unknown graph and soft interventions.
result Regret bounds for unknown graph and soft interventions, with a universal minimax lower bound.
Graph matching in noisy environments with Markovian errors.
problem Graph matching under time-dependent Markovian noise.
method Introduced edgelighter error model and analyzed graph matching thresholds.
result Graph matching thresholds and mixing times are of order Θ(n2logn) for Erdős-Rényi graphs, and O(nαlogn) for Stochastic Block Model graphs.