Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Inductive Graph Few-shot Class Incremental Learning
Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves learning a GNN classifier as graph nodes an…
class-incremental learningClass Incremental LearningFew-Shot Class-Incremental LearningIncremental Learning+1YOSO: You-Only-Sample-Once via Compressed Sensing for Graph Neural Network Training
Graph neural networks (GNNs) have become essential tools for analyzing non-Euclidean data across various domains. During training stage, sampling plays an important role in reducing latency by limiting the number of node…
compressed sensingGraph Neural NetworkLink PredictionNode Classification+1Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields
Graph Neural Networks (GNNs), which are nowadays the benchmark approach in graph representation learning, have been shown to be vulnerable to adversarial attacks, raising concerns about their real-world applicability. Wh…
Graph Representation LearningNode ClassificationRelational ReasoningRepresentation LearningGaGSL: Global-augmented Graph Structure Learning via Graph Information Bottleneck
Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observed structure accurately represents the u…
Graph structure learningNode ClassificationHigher-Order GNNs Meet Efficiency: Sparse Sobolev Graph Neural Networks
Graph Neural Networks (GNNs) have shown great promise in modeling relationships between nodes in a graph, but capturing higher-order relationships remains a challenge for large-scale networks. Previous studies have prima…
Graph MiningNode ClassificationGraph Neural Networks with Coarse- and Fine-Grained Division for Mitigating Label Sparsity and Noise
Graph Neural Networks (GNNs) have gained considerable prominence in semi-supervised learning tasks in processing graph-structured data, primarily owing to their message-passing mechanism, which largely relies on the avai…
Graph ReconstructionNode ClassificationEnhancing the Expressivity of Temporal Graph Networks through Source-Target Identification
Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform poorly on the task of dynamic node affinity prediction -- where t…
Dynamic Node ClassificationLink PredictionNode ClassificationPredictionHigh-Pass Graph Convolutional Network for Enhanced Anomaly Detection: A Novel Approach
Graph Convolutional Network (GCN) are widely used in Graph Anomaly Detection (GAD) due to their natural compatibility with graph structures, resulting in significant performance improvements. However, most researchers ap…
Anomaly DetectionGraph Anomaly DetectionGraph Neural NetworkNode ClassificationG-SPARC: SPectral ARchitectures tackling the Cold-start problem in Graph learning
Graphs play a central role in modeling complex relationships across various domains. Most graph learning methods rely heavily on neighborhood information, raising the question of how to handle cold-start nodes - nodes wi…
Graph LearningLink PredictionNode ClassificationNode ClusteringRAGraph: A General Retrieval-Augmented Graph Learning Framework
Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In thi…
Graph ClassificationGraph LearningLink PredictionNode Classification+1DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification
Graph Neural Networks (GNNs) are susceptible to distribution shifts, creating vulnerability and security issues in critical domains. There is a pressing need to enhance the generalizability of GNNs on out-of-distribution…
Node ClassificationSparse Decomposition of Graph Neural Networks
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of …
Graph Neural NetworkGraph Representation LearningNode ClassificationRepresentation Learning+1Bonsai: Gradient-free Graph Condensation for Node Classification
Graph condensation has emerged as a promising avenue to enable scalable training of GNNs by compressing the training dataset while preserving essential graph characteristics. Our study uncovers significant shortcomings i…
ClassificationNode ClassificationSaVe-TAG: Semantic-aware Vicinal Risk Minimization for Long-Tailed Text-Attributed Graphs
Real-world graph data often follows long-tailed distributions, making it difficult for Graph Neural Networks (GNNs) to generalize well across both head and tail classes. Recent advances in Vicinal Risk Minimization (VRM)…
ClassificationData AugmentationLanguage ModelingLanguage Modelling+3Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
Graph Neural Networks (GNNs) have shown superior performance in node classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) task that requires robust generalization to make accurate predi…
Node ClassificationLEGO-Learn: Label-Efficient Graph Open-Set Learning
How can we train graph-based models to recognize unseen classes while keeping labeling costs low? Graph open-set learning (GOL) and out-of-distribution (OOD) detection aim to address this challenge by training models tha…
Node ClassificationOpen Set LearningOut of Distribution (OOD) DetectionLEX-GNN: Label-Exploring Graph Neural Network for Accurate Fraud Detection
Graph-based fraud detection faces significant challenges, such as severe class imbalance, inconsistent connections due to the scarcity of fraudulent nodes, and the camouflage of these nodes appearing like benign nodes. E…
Fraud DetectionGraph Neural NetworkNode ClassificationFIT-GNN: Faster Inference Time for GNNs Using Coarsening
Scalability of Graph Neural Networks (GNNs) remains a significant challenge, particularly when dealing with large-scale graphs. To tackle this, coarsening-based methods are used to reduce the graph into a smaller graph, …
ClassificationGraph ClassificationGraph RegressionNode Classification+2Learning to Control the Smoothness of Graph Convolutional Network Features
The pioneering work of Oono and Suzuki [ICLR, 2020] and Cai and Wang [arXiv:2006.13318] initializes the analysis of the smoothness of graph convolutional network (GCN) features. Their results reveal an intricate empirica…
ClassificationComputational EfficiencyNode ClassificationImproving Graph Neural Networks by Learning Continuous Edge Directions
Graph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs, which often leads to homogenization of node features and reduced discriminative power in task…
Node Classification