Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Linear Opinion Pooling for Uncertainty Quantification on Graphs
We address the problem of uncertainty quantification for graph-structured data, or, more specifically, the problem to quantify the predictive uncertainty in (semi-supervised) node classification. Key questions in this re…
Node ClassificationUncertainty QuantificationGNNAnatomy: Rethinking Model-Level Explanations for Graph Neural Networks
Graph Neural Networks (GNNs) achieve outstanding performance across graph-based tasks but remain difficult to interpret. In this paper, we revisit foundational assumptions underlying model-level explanation methods for G…
ClassificationcounterfactualDecision MakingDiversity+4Enhancing the Resilience of Graph Neural Networks to Topological Perturbations in Sparse Graphs
Graph neural networks (GNNs) have been extensively employed in node classification. Nevertheless, recent studies indicate that GNNs are vulnerable to topological perturbations, such as adversarial attacks and edge disrup…
Link PredictionNode ClassificationNode-wise Filtering in Graph Neural Networks: A Mixture of Experts Approach
Graph Neural Networks (GNNs) have proven to be highly effective for node classification tasks across diverse graph structural patterns. Traditionally, GNNs employ a uniform global filter, typically a low-pass filter for …
Mixture-of-ExpertsNode ClassificationLearning Long Range Dependencies on Graphs via Random Walks
Message-passing graph neural networks (GNNs) excel at capturing local relationships but struggle with long-range dependencies in graphs. In contrast, graph transformers (GTs) enable global information exchange but often …
Graph ClassificationGraph Property PredictionGraph RegressionGraph Representation Learning+3What Improves the Generalization of Graph Transformers? A Theoretical Dive into the Self-attention and Positional Encoding
Graph Transformers, which incorporate self-attention and positional encoding, have recently emerged as a powerful architecture for various graph learning tasks. Despite their impressive performance, the complex non-conve…
Graph LearningNode ClassificationContinuous Geometry-Aware Graph Diffusion via Hyperbolic Neural PDE
While Hyperbolic Graph Neural Network (HGNN) has recently emerged as a powerful tool dealing with hierarchical graph data, the limitations of scalability and efficiency hinder itself from generalizing to deep models. In …
Graph Neural NetworkImage-text ClassificationLink PredictionNode Classification+2AGALE: A Graph-Aware Continual Learning Evaluation Framework
In recent years, continual learning (CL) techniques have made significant progress in learning from streaming data while preserving knowledge across sequential tasks, particularly in the realm of euclidean data. To foste…
Continual LearningGraph LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Learning to Solve Multiresolution Matrix Factorization by Manifold Optimization and Evolutionary Metaheuristics
Multiresolution Matrix Factorization (MMF) is unusual amongst fast matrix factorization algorithms in that it does not make a low rank assumption. This makes MMF especially well suited to modeling certain types of graphs…
Evolutionary AlgorithmsNode ClassificationTowards a General Recipe for Combinatorial Optimization with Multi-Filter GNNs
Graph neural networks (GNNs) have achieved great success for a variety of tasks such as node classification, graph classification, and link prediction. However, the use of GNNs (and machine learning more generally) to so…
Combinatorial OptimizationGraph ClassificationLink PredictionNode Classification+1Learning on Large Graphs using Intersecting Communities
Message Passing Neural Networks (MPNNs) are a staple of graph machine learning. MPNNs iteratively update each node's representation in an input graph by aggregating messages from the node's neighbors, which necessitates …
Graph LearningLEMMANode ClassificationSign is Not a Remedy: Multiset-to-Multiset Message Passing for Learning on Heterophilic Graphs
Graph Neural Networks (GNNs) have gained significant attention as a powerful modeling and inference method, especially for homophilic graph-structured data. To empower GNNs in heterophilic graphs, where adjacent nodes ex…
Node ClassificationHeterophilous Distribution Propagation for Graph Neural Networks
Graph Neural Networks (GNNs) have achieved remarkable success in various graph mining tasks by aggregating information from neighborhoods for representation learning. The success relies on the homophily assumption that n…
Contrastive LearningGraph MiningNode ClassificationRepresentation LearningFully-inductive Node Classification on Arbitrary Graphs
One fundamental challenge in graph machine learning is generalizing to new graphs. Many existing methods following the inductive setup can generalize to test graphs with new structures, but assuming the feature and label…
ClassificationNode ClassificationSpatio-Spectral Graph Neural Networks
Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop n…
GPUGraph ClassificationGraph Neural NetworkGraph Regression+2Matrix Manifold Neural Networks++
Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been designed to solve a wide range of compute…
Action RecognitionNode ClassificationTemporal Action LocalizationGraph Coarsening with Message-Passing Guarantees
Graph coarsening aims to reduce the size of a large graph while preserving some of its key properties, which has been used in many applications to reduce computational load and memory footprint. For instance, in graph ma…
Node ClassificationUnleashing the Potential of Text-attributed Graphs: Automatic Relation Decomposition via Large Language Models
Recent advancements in text-attributed graphs (TAGs) have significantly improved the quality of node features by using the textual modeling capabilities of language models. Despite this success, utilizing text attributes…
Node ClassificationRelationRepresentation LearningSpectral Greedy Coresets for Graph Neural Networks
The ubiquity of large-scale graphs in node-classification tasks significantly hinders the real-world applications of Graph Neural Networks (GNNs). Node sampling, graph coarsening, and dataset condensation are effective s…
Dataset CondensationNode ClassificationTransfer Learning Under High-Dimensional Graph Convolutional Regression Model for Node Classification
Node classification is a fundamental task, but obtaining node classification labels can be challenging and expensive in many real-world scenarios. Transfer learning has emerged as a promising solution to address this cha…
ClassificationNode ClassificationregressionTransfer Learning