Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Mitigating Degree Bias in Graph Representation Learning with Learnable Structural Augmentation and Structural Self-Attention
Graph Neural Networks (GNNs) update node representations through message passing, which is primarily based on the homophily principle, assuming that adjacent nodes share similar features. However, in real-world graphs wi…
FairnessGraph Representation LearningNode ClassificationNode Clustering+2Hierarchical Vector Quantized Graph Autoencoder with Annealing-Based Code Selection
Graph self-supervised learning has gained significant attention recently. However, many existing approaches heavily depend on perturbations, and inappropriate perturbations may corrupt the graph's inherent information. T…
Link PredictionNode ClassificationQuantizationSelf-Supervised LearningIntegrating Structural and Semantic Signals in Text-Attributed Graphs with BiGTex
Text-attributed graphs (TAGs) present unique challenges in representation learning by requiring models to capture both the semantic richness of node-associated texts and the structural dependencies of the graph. While gr…
Link PredictionNode Classificationparameter-efficient fine-tuningRepresentation LearningBalancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks initially designed for other domains, such as contrastive learning an…
Contrastive LearningGraph EmbeddingLink PredictionNode Classification+1GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector Quantization
Graph Transformers (GTs), which simultaneously integrate message-passing and self-attention mechanisms, have achieved promising empirical results in some graph prediction tasks. Although these approaches show the potenti…
Graph LearningGraph Representation LearningNode ClassificationQuantization+1Trajectory Encoding Temporal Graph Networks
Temporal Graph Networks (TGNs) have demonstrated significant success in dynamic graph tasks such as link prediction and node classification. Both tasks comprise transductive settings, where the model predicts links among…
Graph LearningLink PredictionNode ClassificationTowards Unbiased Federated Graph Learning: Label and Topology Perspectives
Federated Graph Learning (FGL) enables privacy-preserving, distributed training of graph neural networks without sharing raw data. Among its approaches, subgraph-FL has become the dominant paradigm, with most work focuse…
FairnessGraph LearningGraph MiningNode Classification+1GOTHAM: Graph Class Incremental Learning Framework under Weak Supervision
Graphs are growing rapidly, along with the number of distinct label categories associated with them. Applications like e-commerce, healthcare, recommendation systems, and various social media platforms are rapidly moving…
Attributeclass-incremental learningClass Incremental LearningIncremental Learning+3Graph Attention for Heterogeneous Graphs with Positional Encoding
Graph Neural Networks (GNNs) have emerged as the de facto standard for modeling graph data, with attention mechanisms and transformers significantly enhancing their performance on graph-based tasks. Despite these advance…
Graph AttentionLink PredictionNode ClassificationGraph-Based Uncertainty-Aware Self-Training with Stochastic Node Labeling
Self-training has become a popular semi-supervised learning technique for leveraging unlabeled data. However, the over-confidence of pseudo-labels remains a key challenge. In this paper, we propose a novel \emph{graph-ba…
Node ClassificationPseudo LabelUncertainty-Aware Graph Self-Training with Expectation-Maximization Regularization
In this paper, we propose a novel \emph{uncertainty-aware graph self-training} approach for semi-supervised node classification. Our method introduces an Expectation-Maximization (EM) regularization scheme to incorporate…
Node ClassificationPseudo LabelAdapt, Agree, Aggregate: Semi-Supervised Ensemble Labeling for Graph Convolutional Networks
In this paper, we propose a novel framework that combines ensemble learning with augmented graph structures to improve the performance and robustness of semi-supervised node classification in graphs. By creating multiple…
Ensemble LearningNode ClassificationPseudo LabelFast online node labeling with graph subsampling
Large data applications rely on storing data in massive, sparse graphs with millions to trillions of nodes. Graph-based methods, such as node prediction, aim for computational efficiency regardless of graph size. Techniq…
Computational EfficiencyNode ClassificationA Semantic and Clean-label Backdoor Attack against Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have shown excellent performance in graph-structured tasks such as node classification and graph classification. However, recent research has shown that GCNs are vulnerable to a new ty…
Backdoor AttackGraph ClassificationNode ClassificationUnifying Text Semantics and Graph Structures for Temporal Text-attributed Graphs with Large Language Models
Temporal graph neural networks (TGNNs) have shown remarkable performance in temporal graph modeling. However, real-world temporal graphs often possess rich textual information, giving rise to temporal text-attributed gra…
Link PredictionNode ClassificationEfficient and Privacy-Preserved Link Prediction via Condensed Graphs
Link prediction is crucial for uncovering hidden connections within complex networks, enabling applications such as identifying potential customers and products. However, this research faces significant challenges, inclu…
Link PredictionNode ClassificationPredictionPrivacy PreservingWeighted Graph Structure Learning with Attention Denoising for Node Classification
Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort …
ClassificationDenoisingGraph AttentionGraph structure learning+1Unifying Structural Proximity and Equivalence for Enhanced Dynamic Network Embedding
Dynamic network embedding methods transform nodes in a dynamic network into low-dimensional vectors while preserving network characteristics, facilitating tasks such as node classification and community detection. Severa…
Community DetectionNetwork EmbeddingNode ClassificationTowards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement
Long-range dependencies are critical for effective graph representation learning, yet most existing datasets focus on small graphs tailored to inductive tasks, offering limited insight into long-range interactions. Curre…
Graph Representation LearningNode ClassificationRepresentation LearningTransductive LearningOverlap-aware meta-learning attention to enhance hypergraph neural networks for node classification
Although hypergraph neural networks (HGNNs) have emerged as a powerful framework for analyzing complex datasets, their practical performance often remains limited. On one hand, existing networks typically employ a single…
Meta-LearningNode Classification