Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
GNNEvaluator: Evaluating GNN Performance On Unseen Graphs Without Labels
Evaluating the performance of graph neural networks (GNNs) is an essential task for practical GNN model deployment and serving, as deployed GNNs face significant performance uncertainty when inferring on unseen and unlab…
Node ClassificationHetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks
Graphs have emerged as a natural choice to represent and analyze the intricate patterns and rich information of the Web, enabling applications such as online page classification and social recommendation. The prevailing …
Node ClassificationFairness-aware Optimal Graph Filter Design
Graphs are mathematical tools that can be used to represent complex real-world interconnected systems, such as financial markets and social networks. Hence, machine learning (ML) over graphs has attracted significant att…
Decision MakingFairnessNode ClassificationSplitGNN: Spectral Graph Neural Network for Fraud Detection against Heterophily
Fraudsters in the real world frequently add more legitimate links while concealing their direct ones with other fraudsters, leading to heterophily in fraud graphs, which is a problem that most GNN-based techniques are no…
Fraud DetectionGraph Neural NetworkNode ClassificationNeighborhood Homophily-Guided Graph Convolutional Network
Graph neural networks (GNNs) have been proved powerful in graph-oriented tasks. However, many real-world graphs are heterophilous, challenging the homophily assumption of classical GNNs. To solve the universality probl…
Node ClassificationPositive-Unlabeled Node Classification with Structure-aware Graph Learning
Node classification on graphs is an important research problem with many applications. Real-world graph data sets may not be balanced and accurate as assumed by most existing works. A challenging setting is positive-unla…
Anomaly DetectionClassificationGraph LearningNode ClassificationExploring Graph Neural Networks for Indian Legal Judgment Prediction
The burdensome impact of a skewed judges-to-cases ratio on the judicial system manifests in an overwhelming backlog of pending cases alongside an ongoing influx of new ones. To tackle this issue and expedite the judicial…
FairnessGraph Neural NetworkLink PredictionNode Classification+1Pretraining Language Models with Text-Attributed Heterogeneous Graphs
In many real-world scenarios (e.g., academic networks, social platforms), different types of entities are not only associated with texts but also connected by various relationships, which can be abstracted as Text-Attrib…
Graph Neural NetworkLink PredictionNode ClassificationText AugmentationMuseGNN: Interpretable and Convergent Graph Neural Network Layers at Scale
Among the many variants of graph neural network (GNN) architectures capable of modeling data with cross-instance relations, an important subclass involves layers designed such that the forward pass iteratively reduces a …
Graph Neural NetworkNode ClassificationHetero$^2$Net: Heterophily-aware Representation Learning on Heterogenerous Graphs
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of comm…
Node ClassificationRepresentation LearningPrivacy-Preserving Graph Embedding based on Local Differential Privacy
Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks, serious privacy concerns arise when the …
Graph EmbeddingLink PredictionNode ClassificationPrivacy PreservingSignGT: Signed Attention-based Graph Transformer for Graph Representation Learning
The emerging graph Transformers have achieved impressive performance for graph representation learning over graph neural networks (GNNs). In this work, we regard the self-attention mechanism, the core module of graph Tra…
Graph Representation LearningNode ClassificationRepresentation LearningA Local Graph Limits Perspective on Sampling-Based GNNs
We propose a theoretical framework for training Graph Neural Networks (GNNs) on large input graphs via training on small, fixed-size sampled subgraphs. This framework is applicable to a wide range of models, including po…
Node ClassificationLeveraging Large Language Models for Node Generation in Few-Shot Learning on Text-Attributed Graphs
Text-attributed graphs have recently garnered significant attention due to their wide range of applications in web domains. Existing methodologies employ word embedding models for acquiring text representations as node f…
Few-Shot LearningGraph LearningInformation RetrievalNode Classification+1Causality and Independence Enhancement for Biased Node Classification
Most existing methods that address out-of-distribution (OOD) generalization for node classification on graphs primarily focus on a specific type of data biases, such as label selection bias or structural bias. However, a…
ClassificationNode ClassificationSelection biasTopology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation
Hypergraphs, with their capacity to depict high-order relationships, have emerged as a significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have remarkable performance in graph representatio…
Graph Representation LearningNode ClassificationRepresentation LearningHeterophily-Based Graph Neural Network for Imbalanced Classification
Graph neural networks (GNNs) have shown promise in addressing graph-related problems, including node classification. However, conventional GNNs assume an even distribution of data across classes, which is often not the c…
ClassificationGraph Neural Networkimbalanced classificationNode ClassificationNon-backtracking Graph Neural Networks
The celebrated message-passing updates for graph neural networks allow representing large-scale graphs with local and computationally tractable updates. However, the updates suffer from backtracking, i.e., a message flow…
Graph Neural NetworkNode ClassificationStochastic Block ModelEnhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation
Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the availability and quality of node-level f…
Graph ClassificationGraph EmbeddingNode ClassificationSimple GNNs with Low Rank Non-parametric Aggregators
We revisit recent spectral GNN approaches to semi-supervised node classification (SSNC). We posit that state-of-the-art (SOTA) GNN architectures may be over-engineered for common SSNC benchmark datasets (citation network…
BenchmarkingNode Classification