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.
Algorithm identifies missing data distributions in graphical models.
problem Identifying missing data distributions in graphical models with interventionist perspective.
method Tree-based identification algorithm that tracks selection bias and admissible intervention strategies.
result Valid estimating equations for missingness mechanism and complete data distribution.
New method uses neural networks to identify sources from limited data in complex systems.
problem Identifying sources from noisy and limited data in high-dimensional systems.
method Calibrating deep neural network surrogates to ensemble simulations and using Bayesian optimization for source identification.
result Reliable source identification with uncertainty quantification using limited data and auxiliary processes.
Sparse Bayesian learning algorithm for estimating interaction kernels in Motsch-Tadmor model.
problem Data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model.
method Variational framework reformulating kernel identification as a subspace identification problem; sparse Bayesian learning algorithm with informative priors.
result Accurate, robust, and interpretable estimation of interaction kernels across various noise levels and data regimes.
New framework recovers reward and rationality parameters from game behavior.
problem Statistical ambiguity in identifying reward and rationality parameters in competitive games.
method Blind Inverse Game Theory (Blind-IGT) using entropy-regularized Quantal Response Equilibrium and Normalized Least Squares (NLS) estimator.
result Optimal convergence rate of O(N−1/2) for joint parameter recovery. We define a pseudo-inverse for line graphs using linear integer programming.
problem Not all graphs have a corresponding root graph, making the line graph operation non-invertible.
method Propose a linear integer program to edit the smallest number of edges in the line graph to recover a root graph.
result The pseudo-inverse operation is well-behaved and works in practice as shown by empirical experiments.
Identification of a groundwater contaminant source simultaneously with the hydraulic conductivity in highly-heterogeneous media often results in a high-dimensional inverse problem. In this study, a deep autoregressive neural network-based surrogate method is developed for the forward model to allow us to solve efficien…
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…
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.
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.
The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available state- variable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influ…
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.
This tutorial introduces causal modeling methods for researchers.
problem Understanding causal relationships in research studies.
method Integrates potential outcomes and graphical methods for causal modeling.
result Clear notation and practical examples for applied researchers.
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.
A new framework models and simulates multibody systems using factor graphs.
problem Solving kinematic and dynamic problems for multi-body systems.
method Factor graph theory for modeling and simulation of multibody systems.
result The proposed framework provides a unified approach for multibody systems.
We define transit clusters to simplify causal diagrams and preserve their essential properties.
problem Clustering variables in causal diagrams can alter essential properties of causal effects.
method We define transit clusters and provide an algorithm to find them, ensuring they preserve causal effect identifiability.
result Transit clusters simplify causal effect identification and maintain their essential properties.
Optimal strategy found for identifying best arm in bandits with small gap.
problem Best arm identification in two-armed bandits with a fixed budget and small gap.
method Neyman allocation rule augmented with inverse probability weighting.
result Proposed strategy is asymptotically optimal when gap is small.
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.
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.
Neural network solves inverse problem in multiscale mechanics.
problem Identifying elastic properties of random materials.
method Artificial neural networks trained on processed databases.
result Robust identification method validated with synthetic and real data.
Efficiently infers graph edges from genetic similarity data in landscape genetics.
problem Inferring unknown graph edges from genetic similarity data in a heterogeneous landscape.
method Developed an efficient first-order optimization method to solve the inverse landscape genetics problem.
result Our method provides fast and reliable convergence, significantly outperforming existing heuristics.
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…
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.
New method learns soliton dynamics from scattering data without assuming known equations.
problem Deriving soliton dynamics from scattering data without prior knowledge.
method Combining IST with weak-form system identification for data-driven discovery.
result Effective soliton dynamics models derived from observed scattering data.
Study inverse curve shortening flow on hyperbolic plane, classifying solitons.
problem Understanding the behavior of curves in hyperbolic geometry under a specific flow.
method Classifying solitons with respect to vector fields and studying their properties.
result Parabolic solitons are graphs on the y-axis, conformal solitons on the x-axis.
We investigate the relationship between the structure of a discrete graphical model and the support of the inverse of a generalized covariance matrix. We show that for certain graph structures, the support of the inverse covariance matrix of indicator variables on the vertices of a graph reflects the conditional indepe…
Paper introduces variational inference for Bayesian inverse problems with gamma hyperpriors.
problem Bayesian inverse problems with sparse solutions.
method Variational iterative alternating scheme for hierarchical models with gamma hyperpriors.
result Accurate reconstruction and meaningful uncertainty quantification.
We propose an algorithmic framework for convex minimization problems of a composite function with two terms: a self-concordant function and a possibly nonsmooth regularization term. Our method is a new proximal Newton algorithm that features a local quadratic convergence rate. As a specific instance of our framework, w…
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.
A method to describe Riemann surfaces using graph profiles is proposed.
problem Describing Riemann surfaces in a graph-theoretic framework.
method Graph profiles to represent Riemann surfaces, establishing necessary and sufficient conditions for their existence.
result A criterion for the existence of Riemann surfaces with a given signature.
The paper defines surface area for graphs and derives spectral estimates.
problem Understanding connectivity measures and spectral properties of graphs.
method Introducing surface area concepts related to inverse degree and deriving spectral bounds.
result An upper bound on the second eigenvalue for planar graphs.
A framework uses variational Bayes for solving inverse problems efficiently.
problem Solving inverse problems in various dimensions with flexibility and accuracy.
method Variational Bayes approximations with message passing and factor graph approach.
result Efficient algorithm updates for higher dimensions and computational advantage over MCMC.
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.
Unified Bayesian framework for LTV system identification using neural networks and Gaussian Processes.
problem Identifying Linear Time-Varying systems from input-output data.
method Bayesian modeling of impulse response as a stochastic process, using neural networks and Gaussian Processes for inference.
result Framework can infer LTI system properties from a single noisy input-output pair, achieving lower error than classical methods.
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.
The paper classifies shapes of translating solitons for a specific flow.
problem Understanding the shapes of translating solitons in a specific flow.
method Analyzing functions on a unit sphere and solving an ODE.
result Classification of the shapes of translating solitons.
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.