Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
MuGSI: Distilling GNNs with Multi-Granularity Structural Information for Graph Classification
Recent works have introduced GNN-to-MLP knowledge distillation (KD) frameworks to combine both GNN's superior performance and MLP's fast inference speed. However, existing KD frameworks are primarily designed for node cl…
ClassificationGraph ClassificationKnowledge DistillationNode ClassificationLeveraging Contrastive Learning for Enhanced Node Representations in Tokenized Graph Transformers
While tokenized graph Transformers have demonstrated strong performance in node classification tasks, their reliance on a limited subset of nodes with high similarity scores for constructing token sequences overlooks val…
Contrastive LearningNode ClassificationNTFormer: A Composite Node Tokenized Graph Transformer for Node Classification
Recently, the emerging graph Transformers have made significant advancements for node classification on graphs. In most graph Transformers, a crucial step involves transforming the input graph into token sequences as the…
Node ClassificationRepresentation LearningKolmogorov-Arnold Graph Neural Networks
Graph neural networks (GNNs) excel in learning from network-like data but often lack interpretability, making their application challenging in domains requiring transparent decision-making. We propose the Graph Kolmogoro…
Decision MakingGraph ClassificationLink PredictionNode ClassificationKAGNNs: Kolmogorov-Arnold Networks meet Graph Learning
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) lay…
Graph ClassificationGraph LearningGraph RegressionKolmogorov-Arnold Networks+2Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs
The modelling of temporal patterns in dynamic graphs is an important current research issue in the development of time-aware GNNs. Whether or not a specific sequence of events in a temporal graph constitutes a temporal p…
Graph Representation LearningInductive BiasNode ClassificationRepresentation LearningTowards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification
Graph Neural Architecture Search (GNAS) has achieved superior performance on various graph-structured tasks. However, existing GNAS studies overlook the applications of GNAS in resource-constraint scenarios. This paper p…
Graph Neural NetworkNetwork PruningNeural Architecture SearchNode ClassificationGC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
Graph condensation (GC) is an emerging technique designed to learn a significantly smaller graph that retains the essential information of the original graph. This condensed graph has shown promise in accelerating graph …
DenoisingNeural Architecture SearchNode ClassificationNext Level Message-Passing with Hierarchical Support Graphs
Message-Passing Neural Networks (MPNNs) are extensively employed in graph learning tasks but suffer from limitations such as the restricted scope of information exchange, by being confined to neighboring nodes during eac…
Graph ClassificationGraph LearningGraph Property PredictionGraph Regression+1From Overfitting to Robustness: Quantity, Quality, and Variety Oriented Negative Sample Selection in Graph Contrastive Learning
Graph contrastive learning (GCL) aims to contrast positive-negative counterparts to learn the node embeddings, whereas graph data augmentation methods are employed to generate these positive-negative samples. The variati…
Contrastive LearningData AugmentationNode ClassificationSketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity
Graph Neural Networks (GNNs) are widely applied to graph learning problems such as node classification. When scaling up the underlying graphs of GNNs to a larger size, we are forced to either train on the complete graph …
Graph LearningNode ClassificationTAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
In this report, we present TAGLAS, an atlas of text-attributed graph (TAG) datasets and benchmarks. TAGs are graphs with node and edge features represented in text, which have recently gained wide applicability in traini…
Graph Question AnsweringNode ClassificationQuestion AnsweringTAGA Pure Transformer Pretraining Framework on Text-attributed Graphs
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progress in the graph domain remains limited …
Link PredictionNode ClassificationMulti-View Empowered Structural Graph Wordification for Language Models
Significant efforts have been dedicated to integrating the powerful Large Language Models (LLMs) with diverse modalities, particularly focusing on the fusion of language, vision and audio data. However, the graph-structu…
Graph Neural NetworkNode ClassificationThe Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
Graph Neural Networks (GNNs) have become pivotal tools for a range of graph-based learning tasks. Notably, most current GNN architectures operate under the assumption of homophily, whether explicitly or implicitly. While…
Node ClassificationFederated Learning with Limited Node Labels
Subgraph federated learning (SFL) is a research methodology that has gained significant attention for its potential to handle distributed graph-structured data. In SFL, the local model comprises graph neural networks (GN…
Federated LearningNode ClassificationA data-centric approach for assessing progress of Graph Neural Networks
Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on the cases where each node could have mult…
ClassificationMulti-class ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Edge Classification on Graphs: New Directions in Topological Imbalance
Recent years have witnessed the remarkable success of applying Graph machine learning (GML) to node/graph classification and link prediction. However, edge classification task that enjoys numerous real-world applications…
ClassificationEdge ClassificationGraph ClassificationLink Prediction+1Graph Knowledge Distillation to Mixture of Experts
In terms of accuracy, Graph Neural Networks (GNNs) are the best architectural choice for the node classification task. Their drawback in real-world deployment is the latency that emerges from the neighbourhood processing…
Knowledge DistillationMixture-of-ExpertsNode ClassificationGraph Neural Reaction Diffusion Models
The integration of Graph Neural Networks (GNNs) and Neural Ordinary and Partial Differential Equations has been extensively studied in recent years. GNN architectures powered by neural differential equations allow us to …
Node Classification