Repelling random walks improve graph-based sampling efficiency.
problem Efficient graph-based sampling and statistical estimation.
method Induces correlations between trajectories of an ensemble of walkers on a graph, maintaining unbiasedness.
result Improves concentration of statistical estimators on graphs.
Paper forecasts stock correlations using a hybrid model combining graph neural networks and transformers.
problem Improving stock correlation forecasts for better portfolio management.
method Hybrid model combining Transformer and graph attention networks for forecasting residual deviations from historical data.
result The hybrid model reduces correlation forecasting error compared to rolling-window estimates.
Sparse models for high-dimensional linear regression and machine learning have received substantial attention over the past two decades. Model selection, or determining which features or covariates are the best explanatory variables, is critical to the interpretability of a learned model. Much of the current literature…
Proposes C2AF network for multi-view time series classification.
problem Improving multi-view time series classification performance.
method Two-stream structured encoder, graph-based correlation matrix, channel-aware fusion mechanism.
result Extensive experimental results show superior performance over state-of-the-art methods.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
Graph-based multi-view model predicts trading volume movement from various sources.
problem Lack of comprehensive understanding of trading volume movement from different sources.
method Graph-based approach incorporating long-term, short-term, and sudden event information.
result Our method outperforms strong baselines by a large margin.
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%).
This paper tackles traffic volume estimation challenges with a deep learning method.
problem Underdetermined and non-equilibrium traffic flows.
method Graph-based deep learning method with adaptive attention mechanisms.
result The proposed model achieves high accuracy even with low sensor coverage.
We introduce a principled method for the signed clustering problem, where the goal is to partition a graph whose edge weights take both positive and negative values, such that edges within the same cluster are mostly positive, while edges spanning across clusters are mostly negative. Our method relies on a graph-based …
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.
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.
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 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.
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.
Based on a recently proposed q-dependent detrended cross-correlation coefficient ρq, we generalize the concept of minimum spanning tree (MST) by introducing a family of q-dependent minimum spanning trees (qMST) that are selective to cross-correlations between different fluctuation amplitudes and different time…
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.
CSML learns causal structures for few-shot learning.
problem Spurious correlations limit deep learning generalization.
method CSML combines perception, causal induction, and reasoning modules.
result CSML achieves superior few-shot learning across tasks.
Graph neural networks often assume vertex labels are independent, but we show this is rarely true and propose a method to improve predictions.
problem Graph neural networks often assume vertex labels are conditionally independent given their neighborhood features, which is rarely true.
method We model the joint distribution of residuals on vertices with a parameterized multivariate Gaussian and estimate parameters by maximizing the marginal likelihood of the observed labels.
result Our method achieves substantially higher accuracy than competing baselines and can be interpreted as the strength of correlation among connected vertices.
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.
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…
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.
Proposes ML-GCN for multi-label network node representation learning.
problem Complex multi-label networks with correlated labels.
method Two Siamese GCNs model node-label and label-label interactions, integrated under a unified objective function.
result Effective node representation learning with preserved label interactions.
Active graph-based semi-supervised learning (AG-SSL) aims to select a small set of labeled examples and utilize their graph-based relation to other unlabeled examples to aid in machine learning tasks. It is also closely related to the sampling theory in graph signal processing. In this paper, we revisit the original fo…
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.
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.
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.
The graph convolution network (GCN) is a widely-used facility to realize graph-based semi-supervised learning, which usually integrates node features and graph topologic information to build learning models. However, as for multi-label learning tasks, the supervision part of GCN simply minimizes the cross-entropy loss …
GATES improves neural architecture search by modeling operations as information transformation.
problem Improving predictor-based neural architecture search efficiency.
method GATES models operations as information transformation, covering both node and edge cell search spaces.
result GATES boosts sample efficiency and improves predictor performance.
Paper proposes a graph network for EHR data that learns robust representations.
problem Learning robust representations for EHR data with implicit connections.
method Variationally regularized encoder-decoder graph network.
result Model outperforms existing methods in various EHR predictive tasks.
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.
Proposes a new algorithm for graph-based semi-parametric contextual bandits.
problem Non-stationarity in human behavior and social interaction.
method SemiGraphTS algorithm for graph-based semi-parametric reward model.
result Derives an upper bound of cumulative regret for graph-based semi-parametric model.
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.
Graph-based rehearsal improves continual learning performance.
problem Catastrophic forgetting in continual learning models.
method Augmenting a memory array with a learnable random graph to capture pairwise similarities.
result Our model consistently outperforms baselines on task-free continual learning benchmarks.
Paper proposes graph-based separable transforms for video coding.
problem Improving video coding efficiency by better capturing residual block statistics.
method Derives graph-based separable transforms (GBSTs) from line graphs with weights determined by parameters.
result GBSTs achieve about 0.4% average coding gain over existing transforms in VVC.
Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacke…
We consider a family of problems that are concerned about making predictions for the majority of unlabeled, graph-structured data samples based on a small proportion of labeled samples. Relational information among the data samples, often encoded in the graph/network structure, is shown to be helpful for these semi-sup…
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.
Graph-based Bayesian SSL uses graph theory to propagate labels from a few to many unlabeled features.
problem Efficiently propagating labels from a small set of labeled data to a large set of unlabeled data.
method Probabilistic framework using graph theory and Bayesian statistics.
result Mathematical foundations for improving the accuracy and efficiency of label propagation.
Graph-based semi-supervised learning for aspect term extraction.
problem Aspect term detection and sentiment analysis in reviews.
method Label spreading algorithm on graph sparsified by kNN for efficient classification.
result The approach achieves good results with limited labeled data.
Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., m…
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.
Flexible per-class regularization improves binary classifiers.
problem Improving binary classifiers by addressing outliers and class imbalance.
method Graph-based adaptive regularization with flexible per-class thresholds.
result Flexible thresholds improve classifier performance and address class imbalance.
Unified framework for data poisoning attacks in graph-based semi-supervised learning.
problem Data poisoning attacks on graph-based semi-supervised learning.
method Unified formula for data poisoning attacks, specialized algorithms for regression and classification tasks.
result Data poisoning can be effective even with minimal perturbations.
New models infer causal effects from graph-based time-series data.
problem Inferring causal effects from graph-based relational time-series data.
method Proposes causal inference models leveraging graph topology and time-series data.
result Relational time-series causal inference models accurately estimate local causal effects of individual nodes.
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.
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.
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.