Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Force-directed graph embedding with hops distance
Graph embedding has become an increasingly important technique for analyzing graph-structured data. By representing nodes in a graph as vectors in a low-dimensional space, graph embedding enables efficient graph processi…
Graph EmbeddingLink PredictionNode ClassificationSymplectic Structure-Aware Hamiltonian (Graph) Embeddings
In traditional Graph Neural Networks (GNNs), the assumption of a fixed embedding manifold often limits their adaptability to diverse graph geometries. Recently, Hamiltonian system-inspired GNNs have been proposed to addr…
Node ClassificationRiemannian optimizationCurve Your Attention: Mixed-Curvature Transformers for Graph Representation Learning
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage hyperbolic or spherical spaces to learn repres…
Graph ReconstructionGraph Representation LearningNode ClassificationRepresentation LearningTowards Unsupervised Graph Completion Learning on Graphs with Features and Structure Missing
In recent years, graph neural networks (GNN) have achieved significant developments in a variety of graph analytical tasks. Nevertheless, GNN's superior performance will suffer from serious damage when the collected node…
Node ClassificationSelf-Supervised LearningEnd-to-End Learning on Multimodal Knowledge Graphs
Knowledge graphs enable data scientists to learn end-to-end on heterogeneous knowledge. However, most end-to-end models solely learn from the relational information encoded in graphs' structure: raw values, encoded as li…
Knowledge GraphsLink PredictionNode ClassificationWhere Did the Gap Go? Reassessing the Long-Range Graph Benchmark
The recent Long-Range Graph Benchmark (LRGB, Dwivedi et al. 2022) introduced a set of graph learning tasks strongly dependent on long-range interaction between vertices. Empirical evidence suggests that on these tasks Gr…
Graph ClassificationGraph LearningGraph RegressionHyperparameter Optimization+2ConCur: Self-supervised graph representation based on contrastive learning with curriculum negative sampling
Contrastive learning has made breakthrough advancements in graph representation learning, which encourages the representation of positive samples to be close and those of negative samples to be far away. However, existin…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation Learning+1Rank Collapse Causes Over-Smoothing and Over-Correlation in Graph Neural Networks
Our study reveals new theoretical insights into over-smoothing and feature over-correlation in graph neural networks. Specifically, we demonstrate that with increased depth, node representations become dominated by a low…
Node ClassificationBenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks
To handle graphs in which features or connectivities are evolving over time, a series of temporal graph neural networks (TGNNs) have been proposed. Despite the success of these TGNNs, the previous TGNN evaluations reveal…
DiversityLink PredictionNode ClassificationDomain-adaptive Message Passing Graph Neural Network
Cross-network node classification (CNNC), which aims to classify nodes in a label-deficient target network by transferring the knowledge from a source network with abundant labels, draws increasing attention recently. To…
Domain AdaptationGRAPH DOMAIN ADAPTATIONGraph Neural NetworkNode ClassificationLow-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation
Graph Neural Network (GNN) training and inference involve significant challenges of scalability with respect to both model sizes and number of layers, resulting in degradation of efficiency and accuracy for large and dee…
Graph Neural NetworkNode ClassificationQuantizationA Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted Networks
Graph representation learning (GRL) has become a prominent tool for furthering the understanding of complex networks providing tools for network embedding, link prediction, and node classification. In this paper, we prop…
Graph Representation LearningLink PredictionNetwork EmbeddingNode Classification+1Over-Squashing in Graph Neural Networks: A Comprehensive survey
Graph Neural Networks (GNNs) revolutionize machine learning for graph-structured data, effectively capturing complex relationships. They disseminate information through interconnected nodes, but long-range interactions f…
Link PredictionNode ClassificationRecommendation SystemsSurveyClass-Imbalanced Graph Learning without Class Rebalancing
Class imbalance is prevalent in real-world node classification tasks and poses great challenges for graph learning models. Most existing studies are rooted in a class-rebalancing (CR) perspective and address class imbala…
ClassificationGraph LearningNode ClassificationUniversal Graph Continual Learning
We address catastrophic forgetting issues in graph learning as incoming data transits from one to another graph distribution. Whereas prior studies primarily tackle one setting of graph continual learning such as increme…
Continual LearningGraph ClassificationGraph LearningNode ClassificationA Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directions
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios. Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data, underpinning v…
Graph LearningLink PredictionNode ClassificationUnveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs
Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vulnerability to privacy inference attacks restricts their practicality, especially in high-…
Node ClassificationPrivacy PreservingCached Operator Reordering: A Unified View for Fast GNN Training
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However, the sparse nature of GNN computation po…
Graph AttentionGraph ClassificationNode ClassificationEnhancing Graph Transformers with Hierarchical Distance Structural Encoding
Graph transformers need strong inductive biases to derive meaningful attention scores. Yet, current methods often fall short in capturing longer ranges, hierarchical structures, or community structures, which are common …
Graph ClassificationGraph RegressionNode ClassificationClass Label-aware Graph Anomaly Detection
Unsupervised GAD methods assume the lack of anomaly labels, i.e., whether a node is anomalous or not. One common observation we made from previous unsupervised methods is that they not only assume the absence of such ano…
Anomaly DetectionGraph Anomaly DetectionNode Classification