Researchers create benchmarks to compare graph inference methods.
problem Comparing graph inference methods is difficult due to varying downstream tasks.
method Developed benchmarks for various graph tasks.
result Contrasted prominent graph inference techniques.
ABI adapts to graph data for fast, scalable inference.
problem Challenges in inference on graph-structured data.
method Amortized Bayesian Inference (ABI) framework for graph data.
result ABI successfully addresses challenges in graph data inference.
Paper develops an online EM algorithm for graph signal inference from streaming data.
problem Joint inference and clustering of graph signals with non-white excitation.
method Mixture model with low-rank plus sparse prior, online EM algorithm.
result Proposed online EM algorithm converges to MAP solution.
Graphical models use graphs to compactly capture stochastic dependencies amongst a collection of random variables. Inference over graphical models corresponds to finding marginal probability distributions given joint probability distributions. In general, this is computationally intractable, which has led to a quest fo…
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.
APGE protects graph node representations from inference attacks.
problem Privacy leakage in graph embedding methods.
method Adversarial training framework with disentangling and purging mechanisms.
result APGE preserves structural and utility attributes while concealing private information.
Paper improves GNN inference speed and memory usage.
problem Scalability issues in GNNs during inference.
method Graph coarsening techniques for faster inference.
result Significant reduction in inference time and memory usage.
A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficul…
AMES framework selects optimal embedding space for latent graph inference.
problem No principled method for choosing the best embedding space for latent graph inference.
method Differentiable AMES framework using backpropagation to select optimal embedding space.
result Consistently achieves comparable or superior results across multiple datasets.
In this work, we address semi-supervised classification of graph data, where the categories of those unlabeled nodes are inferred from labeled nodes as well as graph structures. Recent works often solve this problem via advanced graph convolution in a conventionally supervised manner, but the performance could degrade …
The random dot product graph (RDPG) is an independent-edge random graph that is analytically tractable and, simultaneously, either encompasses or can successfully approximate a wide range of random graphs, from relatively simple stochastic block models to complex latent position graphs. In this survey paper, we describ…
The paper infers multiple graphs from stationary signals on them.
problem Inferring multiple graphs from signals observed on their nodes.
method Convex optimization method leveraging matrix polynomial commutation.
result High-probability bounds on recovery error provided.
Learned factor graphs improve inference from time sequences using neural networks.
problem Inference from time sequences with limited labeled data.
method Combines model-based algorithms and data-driven ML tools for stationary time sequences.
result Learned factor graphs can accurately infer from small training sets.
Energy savings for DNN inference on resource-constrained devices.
problem Energy efficiency in deep learning inference for constrained devices.
method Efficiently searches through equivalent DNN graphs to find the one with the least execution cost.
result Achieves 24% energy savings with minimal performance impact.
Graph embedding leaks sensitive graph properties and subgraphs.
problem Privacy risks in graph embedding sharing.
method Three inference attacks and a defense mechanism.
result High accuracy in inferring graph properties and subgraphs.
Paper constructs unfaithful probability distributions in binary causal graphs.
problem Unfaithful probability distributions in binary causal graphs.
method Constructs unfaithful probability distributions in binary causal graphs.
result Examples of unfaithful probability distributions in binary causal graphs.
Proposes a graph dynamics prior for more accurate relational inference.
problem Identifying interactions in dynamical systems from observed dynamics.
method Graph Dynamics Prior (GDP) that uses error amplification in non-local polynomial filters.
result Reconstructs graphs more accurately than previous methods, robust to under-sampling.
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.
Enhances graph modeling with hyperbolic geometry and variational inference.
problem Challenges in modeling relational data with complex dependencies.
method Semi-implicit hierarchical variational Bayes with Poincaré embedding and mutual information regularization.
result Improves graph representation quality and flexibility in edge prediction and node classification.
LambdaNet infers TypeScript types using graph neural networks.
problem Automatic inference of TypeScript type annotations.
method Graph Neural Network for type dependency graph analysis.
result LambdaNet outperforms existing methods by 14%.
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.
An autonomous variational inference algorithm for arbitrary graphical models requires the ability to optimize variational approximations over the space of model parameters as well as over the choice of tractable families used for the variational approximation. In this paper, we present a novel combination of graph part…
PGMax automates PGM inference on GPUs, improving quality and speed.
problem Efficient inference in complex discrete PGMs.
method Factor graph specification and loopy belief propagation in JAX.
result Higher-quality inference with up to 3x speedups.
Estimates multiple networks using graphons for non-aligned graphs.
problem Estimating topology of multiple networks from nodal observations.
method Combining maximum likelihood penalty with graphon estimation schemes.
result Validated performance against competing methods in synthetic and real-world datasets.
ProDAG uses variational inference to learn DAGs with uncertainty quantification.
problem Statistical and computational challenges in learning a single DAG from data.
method Bayesian variational inference framework with novel distributions.
result ProDAG outperforms state-of-the-art alternatives in accuracy and uncertainty quantification.
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationship…
While many multiple graph inference methodologies operate under the implicit assumption that an explicit vertex correspondence is known across the vertex sets of the graphs, in practice these correspondences may only be partially or errorfully known. Herein, we provide an information theoretic foundation for understand…
Causal inference improves heterophilic graph learning.
problem Capturing asymmetric node dependencies in graph learning.
method Intervention-based causal inference for graph structure learning.
result CausalMP achieves superior link prediction performance.
Estimates network topologies from shared graphon models across different networks.
problem Estimating the topology of multiple networks from nodal observations.
method Combining maximum likelihood penalty with graphon estimation schemes.
result Validated performance against competing methods in synthetic and real-world datasets.
Attack graphs provide compact representations of the attack paths that an attacker can follow to compromise network resources by analysing network vulnerabilities and topology. These representations are a powerful tool for security risk assessment. Bayesian inference on attack graphs enables the estimation of the risk …
Graph matching is a challenging problem with very important applications in a wide range of fields, from image and video analysis to biological and biomedical problems. We propose a robust graph matching algorithm inspired in sparsity-related techniques. We cast the problem, resembling group or collaborative sparsity f…
In this work, we formalize the problem of causal inference over graph-based relational time-series data where each node in the graph has one or more time-series associated to it. We propose causal inference models for this problem that leverage both the graph topology and time-series to accurately estimate local causal…
We propose a new family of combinatorial inference problems for graphical models. Unlike classical statistical inference where the main interest is point estimation or parameter testing, combinatorial inference aims at testing the global structure of the underlying graph. Examples include testing the graph connectivity…
A new method for graph neural networks speeds up inference and training.
problem Challenges in constructing mini-batches for large graphs in graph neural networks.
method Theoretical model of batch construction via maximizing influence score of nodes on outputs.
result Accelerates inference by up to 130x compared to previous methods.
Paper proposes learnable topological features for efficient phylogenetic inference.
problem Finding appropriate topological structures for phylogenetic inference tasks requires significant design effort and domain expertise.
method Combines raw node features with graph neural networks to automatically adapt to different tasks.
result Demonstrates effectiveness and efficiency on simulated and real data phylogenetic inference tasks.
Bayesian method infers transition matrices from incomplete graph data with topological constraints.
problem Inference of transition matrices from incomplete graph data with topological constraints.
method Bayesian approach using repeated interactions and a topological prior.
result Higher accuracy in inferring transition probabilities, improving downstream tasks.
PIVID infers DAG structures from data using variational inference and permutations.
problem Estimating the structure of Bayesian networks from observational data.
method PIVID uses variational inference and continuous relaxations of discrete distributions to infer a distribution over permutations and DAGs.
result PIVID outperforms deterministic and Bayesian approaches in estimating DAG structures from data.
We consider the problem of providing nonparametric confidence guarantees for undirected graphs under weak assumptions. In particular, we do not assume sparsity, incoherence or Normality. We allow the dimension D to increase with the sample size n. First, we prove lower bounds that show that if we want accurate infe…
We introduce block-tree graphs as a framework for deriving efficient algorithms on graphical models. We define block-tree graphs as a tree-structured graph where each node is a cluster of nodes such that the clusters in the graph are disjoint. This differs from junction-trees, where two clusters connected by an edge al…
New distribution simplifies covariance matrix inference.
problem Efficient inference for covariance matrices in large models.
method Incorporates Inverse G-Wishart distribution for variational message passing.
result Elegant and succinct expression of variational message passing fragments.
Graph Neural Network improves causal inference in dynamic systems.
problem Identifying causal relations among multi-variate time series.
method Graph Neural Network approach with score-based method.
result Graph Neural Network significantly outperformed other methods in dynamic Bayesian network inference.
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.
This chapter covers methods for identifying and inferring graph topologies.
problem Identifying and inferring graph topologies from multidimensional relational data.
method Overview of methods including correlation metrics, covariance selection, kernels, structural equations, and vector autoregressions.
result Supports both batch and online learning with convergence guarantees and leverages high-order statistical information.
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.
The paper analyzes distributed Bayesian inference and its Frequentist guarantees.
problem Analyzing large decentralized datasets with distributed Bayesian inference.
method Establishes Frequentist properties for distributed (non-)Bayesian inference.
result Distributed Bayesian inference retains parametric efficiency and enhances robustness.
This work improves graph inference using the degree-4 sum-of-squares hierarchy.
problem Recovering ground-truth binary labelings from corrupted edge observations.
method Apply the degree-4 sum-of-squares hierarchy to a quadratic combinatorial optimization problem.
result The solution of the dual problem is related to edge weights of Johnson and Kneser graphs.
Causal inference relies on the structure of a graph, often a directed acyclic graph (DAG). Different graphs may result in different causal inference statements and different intervention distributions. To quantify such differences, we propose a (pre-) distance between DAGs, the structural intervention distance (SID). T…
Statistical inference on graphs is a burgeoning field in the applied and theoretical statistics communities, as well as throughout the wider world of science, engineering, business, etc. In many applications, we are faced with the reality of errorfully observed graphs. That is, the existence of an edge between two vert…