Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively investigate the GCN robustness over omnipres…
Contrastive LearningNode ClassificationRDF-star2Vec: RDF-star Graph Embeddings for Data Mining
Knowledge Graphs (KGs) such as Resource Description Framework (RDF) data represent relationships between various entities through the structure of triples (<subject, predicate, object>). Knowledge graph embedding (KGE) i…
BenchmarkingGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+3Graph Coarsening via Convolution Matching for Scalable Graph Neural Network Training
Graph summarization as a preprocessing step is an effective and complementary technique for scalable graph neural network (GNN) training. In this work, we propose the Coarsening Via Convolution Matching (CONVMATCH) algor…
Graph Neural NetworkLink PredictionNode ClassificationPredictionTowards Fine-Grained Explainability for Heterogeneous Graph Neural Network
Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to hum…
Graph Neural NetworkNode ClassificationPC-Conv: Unifying Homophily and Heterophily with Two-fold Filtering
Recently, many carefully crafted graph representation learning methods have achieved impressive performance on either strong heterophilic or homophilic graphs, but not both. Therefore, they are incapable of generalizing …
Graph Representation LearningNode ClassificationRepresentation LearningPUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation
When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is easily overwritten when learning with newly…
Continual LearningGraph LearningGraph Representation LearningNode Classification+1DP-AdamBC: Your DP-Adam Is Actually DP-SGD (Unless You Apply Bias Correction)
The Adam optimizer is a popular choice in contemporary deep learning, due to its strong empirical performance. However we observe that in privacy sensitive scenarios, the traditional use of Differential Privacy (DP) with…
Node ClassificationNodeMixup: Tackling Under-Reaching for Graph Neural Networks
Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-supervised node classification problem. However, due to the uneven location distribution of labeled nodes in the graph, labeled nodes are o…
Node ClassificationChasing Fairness in Graphs: A GNN Architecture Perspective
There has been significant progress in improving the performance of graph neural networks (GNNs) through enhancements in graph data, model architecture design, and training strategies. For fairness in graphs, recent stud…
AttributeContrastive LearningFairnessNode ClassificationPoincaré Differential Privacy for Hierarchy-Aware Graph Embedding
Hierarchy is an important and commonly observed topological property in real-world graphs that indicate the relationships between supervisors and subordinates or the organizational behavior of human groups. As hierarchy …
Graph EmbeddingInductive BiasInference AttackNode Classification+2Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity
Recent research demonstrates that GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. However, they mainly focus on node classificatio…
Active LearningDiversityGraph ClassificationNode ClassificationGraph Transformers for Large Graphs
Transformers have recently emerged as powerful neural networks for graph learning, showcasing state-of-the-art performance on several graph property prediction tasks. However, these results have been limited to small-sca…
Graph LearningGraph Property PredictionNode ClassificationProperty Prediction+1Hypergraph Transformer for Semi-Supervised Classification
Hypergraphs play a pivotal role in the modelling of data featuring higher-order relations involving more than two entities. Hypergraph neural networks emerge as a powerful tool for processing hypergraph-structured data, …
ClassificationNode ClassificationRepresentation LearningGraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy
Graph neural networks (GNNs) have shown advantages in graph-based analysis tasks. However, most existing methods have the homogeneity assumption and show poor performance on heterophilic graphs, where the linked nodes ha…
Deep Reinforcement LearningGraph Neural NetworkNode Classificationreinforcement-learning+1Hypergraph-MLP: Learning on Hypergraphs without Message Passing
Hypergraphs are vital in modelling data with higher-order relations containing more than two entities, gaining prominence in machine learning and signal processing. Many hypergraph neural networks leverage message passin…
Node ClassificationRepresentation LearningDynamic Spiking Framework for Graph Neural Networks
The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented b…
Dynamic Node ClassificationGraph Representation LearningNode ClassificationRepresentation LearningCAT: A Causally Graph Attention Network for Trimming Heterophilic Graph
Local Attention-guided Message Passing Mechanism (LAMP) adopted in Graph Attention Networks (GATs) is designed to adaptively learn the importance of neighboring nodes for better local aggregation on the graph, which can …
Graph AttentionNode ClassificationGraph Neural Networks with Diverse Spectral Filtering
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the identical filter weights to mine their l…
GPRNode ClassificationCurriculum-Enhanced Residual Soft An-Isotropic Normalization for Over-smoothness in Deep GNNs
Despite Graph neural networks' significant performance gain over many classic techniques in various graph-related downstream tasks, their successes are restricted in shallow models due to over-smoothness and the difficul…
Node ClassificationERASE: Error-Resilient Representation Learning on Graphs for Label Noise Tolerance
Deep learning has achieved remarkable success in graph-related tasks, yet this accomplishment heavily relies on large-scale high-quality annotated datasets. However, acquiring such datasets can be cost-prohibitive, leadi…
DenoisingNode ClassificationRepresentation Learning