Investigation of the market graph attracts a growing attention in market network analysis. One of the important problem connected with market graph is to identify it from observations. Traditional way for the market graph identification is to use a simple procedure based on statistical estimations of Pearson correlatio…
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48 results for “graph identification”
Paper tackles dynamic graph topology identification in time-varying graphs.
problem Dynamic graph topology identification in time-varying graphs.
method Proposes an online algorithm for time-varying optimization, with intrinsic temporal regularization.
result Demonstrates performance on Gaussian graphical model problem.
Identifies directed graphs from node measurements using polynomial filters.
problem Inferring directed network topology from nodal measurements.
method System identification of graph convolutional filter followed by topology inference.
result Effective recovery of directed graphs from measurements.
Simplified identification methods for causal inference with arbitrary interventional distributions.
problem Estimating cause-effect relationships from data with experimental interventions.
method Using Single World Intervention Graphs and nested model factorization, we provide algorithms for identifying causal parameters from mixed observational and interventional distributions.
result Our algorithms are complete for certain types of interventional marginal distributions.
Can we identify node labels from graph labels?
problem Identifying node labels from graph labels in a hierarchical network.
method Gaussian Mixture Graph Convolutional Network (GMGCN) with Graph Attention Network (GAT) and Gaussian Mixture Layer (GML).
result The proposed method outperforms other baselines on various benchmarks.
New bounds for causal effect identification in time series graphs with latent confounders.
problem Identifying causal effects in time series graphs with latent confounders over unbounded time intervals.
method Applying the Causal Identification algorithm to a constant-size segment of the time series graph.
result A bound on the number of past time steps needed for causal effect identification.
Identifies causal effects in partially directed acyclic graphs with observed variables.
problem Identifying conditional causal effects in graphs with background knowledge and observed variables.
method Three results: identification formula, do calculus generalization, and algorithm completeness.
result Complete algorithm for identifying conditional effects in MPDAGs.
New algorithm identifies causal relationships from graphs, even with selection bias.
problem Identifying causal relationships from graphs with selection bias.
method Developed a measure-theoretic version of Pearl's causal calculus and a sound, complete identification algorithm.
result General measure-theoretic version of causal calculus allows for identification of causal relationships under selection bias.
Paper tackles efficient BAI in graph-smooth bandits.
problem Best arm identification with graph smoothness constraint.
method Gradient ascent algorithm for sample complexity.
result Asymptotically optimal strategy for BAI.
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.
Front-door Reducibility: Reducing ADMGs to the Standard Front-door Setting via a Graphical Criterionstat.ML
FDR criterion simplifies complex causal graphs to a standard front-door setting.
problem Complex causal graphs make identification of causal effects difficult and computationally infeasible.
method Front-door reducibility (FDR) criterion and FDR-TID algorithm.
result Many graphs can be simplified to a standard front-door setting, making causal effect identification simpler and more interpretable.
Bayesian method improves online NARMAX model identification.
problem Online identification of nonlinear systems with small sample sizes and low noise.
method Variational Bayesian inference using message passing algorithm for polynomial NARMAX models.
result Variational Bayesian estimator outperforms recursive and offline least-squares methods.
This paper introduces a novel graph signal processing framework for building graph-based models from classes of filtered signals. In our framework, graph-based modeling is formulated as a graph system identification problem, where the goal is to learn a weighted graph (a graph Laplacian matrix) and a graph-based filter…
We introduce a new family of graphical models that consists of graphs with possibly directed, undirected and bidirected edges but without directed cycles. We show that these models are suitable for representing causal models with additive error terms. We provide a set of sufficient graphical criteria for the identifica…
Missing data is a pervasive problem in data analyses, resulting in datasets that contain censored realizations of a target distribution. Many approaches to inference on the target distribution using censored observed data, rely on missing data models represented as a factorization with respect to a directed acyclic gra…
Enhances graph neural networks by creating virtual data examples.
problem Lack of examples to identify optimal graph rationales in graph applications.
method Introduces environment replacement to create virtual data examples and proposes a framework for rationale-environment separation and representation learning.
result Demonstrates the effectiveness and efficiency of the augmentation-based graph rationalization framework on molecular and polymer datasets.
Bayesian Cox model identifies biomarkers from multi-omics data.
problem Produce interpretable survival prognosis from multi-omics data.
method Penalized semiparametric Bayesian Cox model with graph-structured selection priors.
result Model identifies new biomarkers and improves survival prediction.
Paper introduces a new method to identify brain hubs using both structural and functional connectivity.
problem Hub node identification in brain networks using only functional connectivity.
method Graph signal processing framework that models functional activity as graph signals on structural connectivity.
result The proposed GraFHub framework identifies hub nodes more accurately than conventional methods.
Computational identifiability is a new framework for identifying causal effects.
problem Identifying causal effects in complex scenarios.
method A computational search procedure for empirical estimators.
result Fine-grained identification questions can be answered.
GNN improves financial risk detection in dynamic networks.
problem Complex, changing financial networks make traditional risk identification methods ineffective.
method Graph Neural Networks (GNN) for embedded representation learning of financial data.
result GNN enhances the detection of hidden risks and abnormal behaviors in financial networks.
Graph neural network explainer identifies causal subgraphs ensuring predictions.
problem Spurious correlations in GNN explainers.
method Proposes {
ame}, a GNN causal explainer via causal inference.
result Significantly outperforms existing GNN explainers in exact groundtruth explanation identification.
Graph learning method improves brain state classification.
problem Classifying brain states from iEEG signals.
method Representation learning on graphs for time-varying brain networks.
result 9.13% improvement in AUC for seizure vs. non-seizure classification.
A new test detects noise in graph data, useful for forecasting.
problem Detecting uncorrelated noise in graph data.
method Spatio-temporal extension of traditional tests, using graph signals.
result Asymptotic distribution known, no assumption of identically distributed data.
Study identifies cancer genes through graph anomaly analysis of protein interactions.
problem Insufficient modeling of biological information in protein interaction networks for cancer gene identification.
method Proposes HIerarchical-Perspective Graph Neural Network (HIPGNN) to detect weight heterogeneity and spectral flattening in cancer gene nodes.
result HIPGNN detects weight heterogeneity and spectral flattening, leading to improved cancer gene identification.
IIC decouples causal identification into two phases, significantly reducing the HTC gap in linear SEMs.
problem Determining causal effect coefficients in linear SEMs with latent confounders using the Half-Trek Criterion (HTC) leaves a gap of inconclusive causal effects.
method Iterative Identification Closure (IIC) framework that decouples causal identification into two phases: a seed function S_0 and Reduced HTC propagation.
result IIC strictly subsumes both HTC and ancestor decomposition, reducing the HTC gap by over 80% with combined seeds.
New assumptions help identify causal relationships in data.
problem Challenges in identifying causal relationships from observational data.
method Introduced typed directed acyclic graphs to constrain causal relationships.
result The proposed assumptions lead to significant gains in causal graph identification.
Vulnerability identification is crucial to protect the software systems from attacks for cyber security. It is especially important to localize the vulnerable functions among the source code to facilitate the fix. However, it is a challenging and tedious process, and also requires specialized security expertise. Inspir…
Graph Laplace operators uniquely identify metrics and densities on manifolds.
problem Identifying Riemannian metrics and sampling densities from graph Laplace operators.
method Analyzing intrinsic and extrinsic graph Laplace operators on compact Riemannian manifolds.
result Graph Laplace operators uniquely determine metrics and densities under certain conditions.
New framework for 3D spatial topology enumeration and identification.
problem Efficient navigation through complex engineering system topologies.
method Mathematical spatial graph theory to represent, enumerate, and identify unique topological classes.
result Identification of distinctive 3D topological classes for engineering systems.
Consistent partial identification of causal effects proved for neural models.
problem Consistency of neural causal partial identification methods.
method Proving consistency for neural models with continuous and categorical variables, considering architecture design and Lipschitz regularization.
result Proven consistency of partial identification via neural causal models in a general setting.
Graphs predict reaction conditions for organic chemistry.
problem Predicting specific reaction conditions in organic chemistry.
method Graph Neural Networks (GNNs) for modeling reaction graphs.
result GNNs can identify specific graph features affecting reaction conditions.
New method identifies latent causal graphs without parametric assumptions.
problem Identifying latent causal graphs without parametric assumptions.
method Constructive proofs with new graphical concepts.
result Conditions for nonparametric identification of latent causal graphs.
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…
This paper studies large-scale dynamical networks where the current state of the system is a linear transformation of the previous state, contaminated by a multivariate Gaussian noise. Examples include stock markets, human brains and gene regulatory networks. We introduce a transition matrix to describe the evolution, …
funcGNN uses graph neural networks to estimate program similarity efficiently.
problem Estimating accurate program similarity for software engineering tasks.
method funcGNN trains on labeled CFG pairs to predict GED between unseen programs using effective embedding vectors.
result funcGNN achieves lower error rate (0.00194) and is 23 times faster than traditional methods.
The Duffing oscillator's parameters are identified online using variational message passing.
problem Estimating parameters of a nonlinear Duffing oscillator in real-time.
method Variational message passing on a factor graph of the Duffing oscillator's generative model.
result The online inference procedure performs as well as offline methods.
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the graph representation alone. However, in many real-world applications, the side information …
Study efficient graph optimization with noisy data.
problem Optimizing functions on graphs with noisy observations.
method Best-arm identification and simulated annealing variants.
result Near-optimal solutions found with small query numbers.
We focus on spectral clustering of unlabeled graphs and review some results on clustering methods which achieve weak or strong consistent identification in data generated by such models. We also present a new algorithm which appears to perform optimally both theoretically using asymptotic theory and empirically.
Polynomial-time methods count and sample DAGs from Markov classes.
problem Counting and sampling Markov equivalent DAGs.
method Polynomial-time algorithms for DAGs from Markov classes.
result Long-standing open problem solved, making practical infeasible strategies feasible.
Relational Structural Causal Models enable causal reasoning about unseen object combinations.
problem Developing a model that can reason about causal and combinatorial aspects of unseen object combinations.
method Relational Structural Causal Models extend structural causal models to include relational variables and define identification criteria.
result Proposed relational neural causal models outperform non-relational baselines on simulated traffic scenes.
This research tackles sample complexity in causal graph recovery with temporal heterogeneity.
problem Recovering a unique causal graph from observational data with temporal heterogeneity.
method Integrates time-series dynamics and multi-environment heterogeneity to constrain the problem, enabling a rigorous analysis of statistical limits.
result Unified necessary identifiability conditions and explicit information-theoretic bounds quantify the sample complexity under different noise distributions.
Learning a graph with a specific structure is essential for interpretability and identification of the relationships among data. It is well known that structured graph learning from observed samples is an NP-hard combinatorial problem. In this paper, we first show that for a set of important graph families it is possib…
Proposes clustering and pruning to simplify causal data fusion models.
problem Combining observational and experimental data to identify causal effects.
method Generalizes pruning and clustering operations for multiple data sources.
result Derives conditions for inferring causal effects from simplified models.
New distances for causal graphs improve evaluation of learned structures.
problem Difficulty in evaluating graphs learned by causal discovery algorithms.
method Developed a framework for causal distances, including new reachability algorithms.
result Improved distances are faster and more scalable than existing methods.
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 …
Study identifies parameters in causal models with latent confounding.
problem Parameter identification in linear non-Gaussian causal models with latent confounding.
method Graphical criterion for necessary and sufficient identifiability of direct causal effects, with polynomial-time algorithm.
result Developed a graphical criterion for identifying direct causal effects in latent variable models with arbitrary non-linear confounding.
The paper compares PINN methods for solving drift-diffusion equations on metric graphs.
problem Solving drift-diffusion equations on metric graphs using machine learning.
method Comparison of physics-informed neural networks (PINNs) for solving drift-diffusion equations on metric graphs.
result PINNs offer a flexible and versatile tool for solving parameter identification or optimization problems on metric graphs.