Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Structural Invariance Matters: Rethinking Graph Rewiring through Graph Metrics
Graph rewiring has emerged as a key technique to alleviate over-squashing in Graph Neural Networks (GNNs) and Graph Transformers by modifying the graph topology to improve information flow. While effective, rewiring inhe…
Node ClassificationLearning Noise-Resilient and Transferable Graph-Text Alignment via Dynamic Quality Assessment
Pre-training Graph Foundation Models (GFMs) on text-attributed graphs (TAGs) is central to web-scale applications such as search, recommendation, and knowledge discovery. However, existing CLIP-style graph-text aligners …
Representation LearningCross-Modal RetrievalNode ClassificationLink PredictionTraining Diverse Graph Experts for Ensembles: A Systematic Empirical Study
Graph Neural Networks (GNNs) have become essential tools for learning on relational data, yet the performance of a single GNN is often limited by the heterogeneity present in real-world graphs. Recent advances in Mixture…
Node ClassificationUniGTE: Unified Graph-Text Encoding for Zero-Shot Generalization across Graph Tasks and Domains
Generalizing to unseen graph tasks without task-specific supervision is challenging: conventional graph neural networks are typically tied to a fixed label space, while large language models (LLMs) struggle to capture gr…
Zero-shot GeneralizationGraph ClassificationNode ClassificationGraph RegressionToward General Digraph Contrastive Learning: A Dual Spatial Perspective
Graph Contrastive Learning (GCL) has emerged as a powerful tool for extracting consistent representations from graphs, independent of labeled information. However, existing methods predominantly focus on undirected graph…
Contrastive LearningNode ClassificationLink PredictionInferred global dense residue transition graphs from primary structure sequences enable protein interaction prediction via directed graph convolutional neural networks
Introduction Accurate prediction of protein-protein interactions (PPIs) is crucial for understanding cellular functions and advancing drug development. Existing in-silico methods use direct sequence embeddings from Prote…
Graph Representation LearningNode ClassificationLink PredictionFUSE: Fast Semi-Supervised Node Embedding Learning via Structural and Label-Aware Optimization
Graph-based learning is a cornerstone for analyzing structured data, with node classification as a central task. However, in many real-world graphs, nodes lack informative feature vectors, leaving only neighborhood conne…
Node ClassificationMulti-View Graph Feature Propagation for Privacy Preservation and Feature Sparsity
Graph Neural Networks (GNNs) have demonstrated remarkable success in node classification tasks over relational data, yet their effectiveness often depends on the availability of complete node features. In many real-world…
Node ClassificationGraph LearningPreference-driven Knowledge Distillation for Few-shot Node Classification
Graph neural networks (GNNs) can efficiently process text-attributed graphs (TAGs) due to their message-passing mechanisms, but their training heavily relies on the human-annotated labels. Moreover, the complex and diver…
Knowledge DistillationNode ClassificationFew-Shot LearningHeSRN: Representation Learning On Heterogeneous Graphs via Slot-Aware Retentive Network
Graph Transformers have recently achieved remarkable progress in graph representation learning by capturing long-range dependencies through self-attention. However, their quadratic computational complexity and inability …
Graph Representation LearningNode ClassificationTAWRMAC: A Novel Dynamic Graph Representation Learning Method
Dynamic graph representation learning has become essential for analyzing evolving networks in domains such as social network analysis, recommendation systems, and traffic analysis. However, existing continuous-time metho…
Graph Representation LearningDynamic Link PredictionRecommendation SystemsNode ClassificationAre Heterogeneous Graph Neural Networks Truly Effective for Node Classification? A Causal Perspective
Graph neural networks (GNNs) have achieved remarkable success in node classification. Building on this progress, heterogeneous graph neural networks (HGNNs) integrate relation types and node and edge semantics to leverag…
Node ClassificationAnalyzing the Effect of Embedding Norms and Singular Values to Oversmoothing in Graph Neural Networks
In this paper, we study the factors that contribute to the effect of oversmoothing in deep Graph Neural Networks (GNNs). Specifically, our analysis is based on a new metric (Mean Average Squared Distance - $MASED$) to qu…
Node ClassificationDetecting LLM-Generated Spam Reviews by Integrating Language Model Embeddings and Graph Neural Network
The rise of large language models (LLMs) has enabled the generation of highly persuasive spam reviews that closely mimic human writing. These reviews pose significant challenges for existing detection systems and threate…
Graph Neural NetworkNode ClassificationFeature EngineeringSpam detectionAdaptive Graph Coarsening for Efficient GNN Training
We propose an adaptive graph coarsening method to jointly learn graph neural network (GNN) parameters and merge nodes via K-means clustering during training. As real-world graphs grow larger, processing them directly bec…
Graph Neural NetworkNode ClassificationStealthy Yet Effective: Distribution-Preserving Backdoor Attacks on Graph Classification
Graph Neural Networks (GNNs) have demonstrated strong performance across tasks such as node classification, link prediction, and graph classification, but remain vulnerable to backdoor attacks that implant imperceptible …
Graph ClassificationNode ClassificationAnomaly DetectionLink PredictionNode Classification via Simplicial Interaction with Augmented Maximal Clique Selection
Considering higher-order interactions allows for a more comprehensive understanding of network structures beyond simple pairwise connections. While leveraging all cliques in a network to handle higher-order interactions …
Node ClassificationGraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning
The class imbalance problem refers to the disproportionate distribution of samples across different classes within a dataset, where the minority classes are significantly underrepresented. This issue is also prevalent in…
Node ClassificationPure Node Selection for Imbalanced Graph Node Classification
The problem of class imbalance refers to an uneven distribution of quantity among classes in a dataset, where some classes are significantly underrepresented compared to others. Class imbalance is also prevalent in graph…
Node ClassificationStructure-Attribute Transformations with Markov Chain Boost Graph Domain Adaptation
Graph domain adaptation has gained significant attention in label-scarce scenarios across different graph domains. Traditional approaches to graph domain adaptation primarily focus on transforming node attributes over ra…
GRAPH DOMAIN ADAPTATIONNode Classification