Graph-based methods for anomaly detection and semi-supervised learning.
problem Detecting unusual clinical actions and anomalies in hospital data.
method Label propagation, harmonic solution, regularization, graph connectivity analysis.
result Effective anomaly detection and semi-supervised learning methods for healthcare data.
Galerkin method outperforms graph-based methods in spectral decompositions.
problem Improving spectral decomposition methods in machine learning.
method Restricting study to a small set of test functions using the Galerkin method.
result Statistical and computational superiority of Galerkin method over graph-based approaches.
Survey of methods to incorporate external knowledge into stock price prediction.
problem Challenges in predicting stock prices due to market volatility and non-linearity.
method Survey of methods for acquiring and incorporating external knowledge into stock price prediction models.
result Systematic synthesis of previous studies on external knowledge types and their application in stock price prediction.
Improved phone classification accuracy using graph-based regularization.
problem Phone classification with limited labeled data.
method Graph-based semi-supervised learning with stochastic entropic regularization.
result Significantly improved phone classification accuracy with low labeled data.
Improves graph-based active learning for non-Gaussian models.
problem Efficiently selecting data points for labeling in graph-based semi-supervised learning.
method Approximates non-Gaussian distributions, introduces rank-one update and model change acquisition function.
result Enhanced active learning for graph-based SSL under non-Gaussian models.
New algorithm improves graph-based active learning by identifying unexplored regions.
problem Improving graph-based active learning by identifying unexplored regions.
method Poisson Reweighted Laplacian Uncertainty Sampling (PWLL) with a diagonal perturbation.
result PWLL effectively identifies unexplored regions in graph-based data.
Paper develops a method to identify graphs and filters from filtered signals.
problem Learning graphs and filters from filtered signals.
method Developed an algorithm to jointly identify a graph and a graph-based filter (GBF) from multiple signal/data observations.
result The proposed algorithm outperforms current state-of-the-art methods.
Graph-based weather prediction adapted for local models.
problem Applying neural weather prediction to limited area modeling.
method Adapting graph-based Neural Weather Prediction approach to local models.
result Validation of multi-scale hierarchical model extension for Nordic region.
Paper proves supermodularity of AG-SSL objective and proposes a greedy sampling algorithm.
problem Improving semi-supervised learning with limited labeled data.
method Proves supermodularity of AG-SSL objective under Stieltjes regularization and proposes a greedy sampling algorithm.
result Proposed method achieves superior classification accuracy compared to state-of-the-art methods.
Paper presents a graph-based semi-supervised method for hyperspectral image classification.
problem Hyperspectral image classification with limited labeled data.
method Novel superpixel algorithm based on spectral covariance matrix, followed by superpixel graph construction and classification.
result The method outperforms state-of-the-art approaches, especially in scenarios with minimal labeled data.
New graph-based method selects outlier ensemble components.
problem Poor components negatively affect consensus results in outlier ensembles.
method Mapping rankings to graphs, mining to identify subsets.
result Our method outperforms state-of-the-art techniques.
The paper analyzes consistency of graph-based semi-supervised learning methods for binary and multi-class classification.
problem Consistency of semi-supervised learning algorithms on graphs with noisy labels and well-clustered unlabelled data.
method The study examines graph-based probit and one-hot encoding methods for binary and multi-class classification, analyzing the consistency of optimization-based techniques.
result The analysis reveals insights into the rational function choice for optimization, improving the consistency of semi-supervised learning algorithms.
The paper proves the consistency of graph-based semi-supervised learning.
problem Proving the consistency of graph-based semi-supervised learning.
method Non-parametric framework, enforcing estimated scores to observed responses for labeled data, tuning parameter for unenforced scores.
result Consistency of graph-based learning is proved under certain conditions.
Graph-based method predicts edge flows from partial measurements.
problem Predicting edge flows from limited measurements.
method Graph-based semi-supervised learning with flow conservation constraints.
result Strong performance on synthetic and real-world flow networks.
Graph-based active learning improves with a new algorithm that balances exploration and exploitation.
problem Graph-based active learning algorithms based on expected error minimization (EEM) often use approximations due to computational hardness, leading to suboptimal performance.
method Proposes TSA (Two-Step Approximation) algorithm that efficiently balances exploration and exploitation with similar computational complexity.
result Empirically shows that balancing exploration and exploitation improves performance in both toy and real-world datasets.
Flexible framework for semi-supervised learning on graphs.
problem Predicting unlabeled graph data using limited labeled data.
method Generative framework leveraging features, graph structure, and labels.
result Outperforms state-of-the-art models in most settings.
Bayesian models for uncertainty in graph-based data classification.
problem Uncertainty quantification in high-dimensional data classification.
method Bayesian models based on graph semi-supervised learning.
result Unified framework for various classification methods.
Proposes a method to learn a low-rank kernel matrix for graph-based clustering.
problem Challenges in learning an optimal kernel matrix for graph-based clustering.
method Unified framework for graph construction and kernel learning, focusing on a low-rank kernel matrix.
result Efficacy of the proposed method validated through extensive experiments.
A scalable graph-based SSL method for large-scale data with few labels.
problem Challenges in semi-supervised learning with limited labeled data and large unlabeled data.
method Constructs a graph from a small set of high-dense vertexes to learn relationships and improve performance.
result Achieves good classification performance, especially with few labels.
Novel graph-based framework for hyperspectral image classification using superpixels.
problem High classification accuracy with limited labelled data in hyperspectral images.
method Superpixel method for defining local regions, spectral and spatial features extraction, contracted graph representation, semi-supervised classifier.
result Our approach produces accurate classifications with minimal labelled data, outperforming state-of-the-art techniques.
A new graph-based clustering method for moderate-dimensional data.
problem Performance degradation of existing graph-based clustering methods in high dimensions.
method Introduces UN-CCDs using NND-based MC-SRT for covering radii determination.
result UN-CCDs provide stable and competitive performance in moderate-sized datasets.
The paper extends graph-based semi-supervised learning to infinite-dimensional Wasserstein space.
problem Graph-based semi-supervised learning in high-dimensional data.
method Laplace Learning in the Wasserstein space, proving variational convergence and characterizing the Laplace-Beltrami operator.
result Consistent classification performance in high-dimensional settings.
Poisson learning improves graph-based semi-supervised learning at very low label rates.
problem Degeneracy of Laplacian semi-supervised learning at low label rates.
method Replaces label assignment with source and sink placement, solving Poisson equation.
result Provably more stable and informative predictions than Laplacian learning.
Proposes CI-GMVC to improve graph-based multi-view clustering performance.
problem Inconsistency in multi-view data affects clustering performance.
method Integrates consistent and inconsistent parts of multiple views using a unified matrix.
result Demonstrates improved clustering performance on real-world datasets.
New optimization method for graph-based learning problems.
problem Minimizing decomposable submodular functions in graph and hypergraph settings.
method Dual strategy and random coordinate descent with projections.
result RCD algorithm converges linearly and achieves significant improvements in prediction accuracy.
GraphFL tackles semi-supervised node classification on graphs using federated learning.
problem Real-world graph-based problems often require collecting the entire graph and labeling a reasonable number of labels, which is impractical and costly.
method GraphFL is a federated learning framework that addresses non-IID data, new label domains, and unlabeled data issues in graph-based semi-supervised node classification.
result GraphFL significantly outperforms compared FL baselines and self-training methods.
This paper detects function-level obfuscation in binary code using graph-based methods.
problem Detecting and characterizing function-level obfuscation in binary code.
method Graph-based approaches, including GNNs, are compared on various datasets.
result GNNs outperform baselines in function-level obfuscation detection, especially in a 11-class classification task.
A new MKL framework improves graph-based clustering and semi-supervised classification.
problem MKL methods often fail to improve performance over single kernels.
method Proposes a new MKL framework based on consensus kernels and automatic weight assignment.
result The proposed method outperforms existing MKL methods on multiple benchmark datasets.
Study examines unsupervised and graph-based methods for anomaly detection in IoBT, outperformed by supervised stacking ensemble.
problem Anomaly detection in adversarial environments of IoBT.
method Unsupervised learning, graph-based methods, ensemble supervised learning, adversarial training.
result Supervised stacking ensemble method outperforms unsupervised and graph-based methods in detecting anomalies.
Novel graph-based approach segments financial integration eras.
problem Assessing worldwide financial integration using data patterns.
method 3-step approach combining graph-based representations and optimization.
result Endogenous stable eras of world-wide financial integration found.
Improved graph-based semi-supervised learning with model change active learning.
problem Identifying which unlabelled data points to label to best improve classifier performance.
method Pairing model change active learning with graph-based semi-supervised learning methods.
result Improved multiclass classification performance over prior methods.
Shapley Flow interprets model predictions using a graph-based approach to feature importance.
problem Existing feature importance methods ignore or hide feature dependencies.
method Shapley Flow considers the entire causal graph and assigns credit to edges.
result Shapley Flow provides a deeper, graph-based view of feature importance.
Bayesian analysis shows unlabeled data improve graph-based semi-supervised learning.
problem Improving semi-supervised learning with limited labeled data.
method Bayesian nonparametric approach using unlabeled data for graph-based learning.
result Posterior contracts optimally around the truth with sufficient unlabeled data.
AUC-spec optimizes graph-based SSL for complex label distributions.
problem Training accurate models with scarce labeled data and abundant unlabeled data.
method Computes a low-dimensional representation that maximizes class separation via AUC optimization.
result AUC-spec achieves competitive results on synthetic and real-world datasets.
Novel online graph-based method detects changes in high-dimensional data.
problem Challenges in detecting changes in high-dimensional data.
method Graph-based similarity measure derived from graph-spanning ratio.
result High detection power and controlled false alarm rate for high-dimensional data.
Proposes a graph-based text representation for improved sentiment analysis.
problem Lack of effective methods to encode semantic relations in textual data for sentiment analysis.
method Sentence-level graph-based text representation with deep neural network.
result Significantly outperforms existing sentiment analysis approaches on benchmark datasets.
Graph-based MAB system improves recommendation accuracy.
problem Improving recommendation accuracy in user space.
method Proposes a graph-based recommendation system that learns user space geometry.
result Simulation results show improvements over state-of-the-art MAB algorithms.
Proposes a new method for big portfolio selection using graph-based conditional moments.
problem Challenges in selecting portfolios for thousands of stocks.
method Graph-based Conditional Moments (GRACE) method: learns quantiles, means, variances, skewness, and kurtosis of stock returns.
result Shows superior performance compared to competitors, especially in measures of conditional variance, skewness, and kurtosis.
Paper detects changes in graph-based data streams using likelihood-ratios.
problem Detecting changes in synchronized graph-based data streams.
method Kernel-based likelihood-ratio estimation over graph nodes.
result Effective detection and localization of change-points.
End-to-end graph-based SSL learns all graph factors dynamically.
problem Learning quality of graph in SSL is crucial but difficult.
method Proposes an end-to-end approach to optimize all graph factors.
result Demonstrates effectiveness on benchmark datasets.
Graph-based ML improves defect prediction in software development.
problem Challenges in predicting defect-prone changes in complex software development.
method Building contribution graphs from developers and source files, using graph-based ML for defect prediction.
result Graph-based ML leads to significantly better defect prediction (F1 score up to 77.55%, MCC up to 53.16%).
A deep learning method for estimating discrete conditional distributions efficiently.
problem Estimating discrete conditional probability distributions efficiently.
method Smoothed dyadic partitioning and graph-based smoothing.
result Significantly reduces error in conditional distribution estimation.
GTDL methods fail to accurately model feature interactions in tabular data.
problem Accurate modeling of feature interactions in tabular data.
method Graph-based tabular deep learning methods using attention mechanisms and message-passing schemes.
result Current GTDL methods fail to recover meaningful feature interactions due to poor edge recovery.
Graph-based method predicts business conduct risk from incomplete data.
problem Sparse and biased data limits risk assessment.
method Visibility-aware GCNII framework on corporate graph.
result Graph-based approach outperforms non-graph methods in predicting future incidents.
Paper introduces Sparse-HFS for large-scale SSL, improving spectral approximation and generalization.
problem Scaling issues in semi-supervised learning with large datasets.
method Edge-sparsification algorithm to construct spectrally similar graphs.
result Theoretical bounds on generalization error for graph-based SSL.
Proposes a graph-based system for personalized news recommendation considering multiple user behaviors.
problem Lack of considering multiple user behaviors in news recommendation systems.
method Builds an interaction behavior graph, applies DeepWalk and G-CNN for news and behavior sequence representations, introduces core and coritivity features.
result Achieves personalized news recommendation considering user's concentration degree of interests.
A new method for multi-agent planning on graphs outperforms existing approaches.
problem Planning coordination among multiple interacting agents on a graph.
method Variational perturbation theory applied to inference in large networks.
result Our method outperforms state-of-the-art methods in non-local cost function scenarios.
Poisoning attacks improve graph-based recommender system recommendations.
problem Designing effective poisoning attacks for graph-based recommender systems.
method Formulated as an optimization problem, solved with techniques to assign rating scores to fake users.
result Outperforms existing attacks for graph-based recommender systems, recommending target items to 580 times more normal users.