Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Geometric instability of graph neural networks on large graphs
We analyse the geometric instability of embeddings produced by graph neural networks (GNNs). Existing methods are only applicable for small graphs and lack context in the graph domain. We propose a simple, efficient and …
Link PredictionNode ClassificationTranslationInvestigating the Interplay between Features and Structures in Graph Learning
In the past, the dichotomy between homophily and heterophily has inspired research contributions toward a better understanding of Deep Graph Networks' inductive bias. In particular, it was believed that homophily strongl…
Graph LearningInductive BiasInformativenessNode ClassificationDisparity, Inequality, and Accuracy Tradeoffs in Graph Neural Networks for Node Classification
Graph neural networks (GNNs) are increasingly used in critical human applications for predicting node labels in attributed graphs. Their ability to aggregate features from nodes' neighbors for accurate classification als…
FairnessNode ClassificationTransitivity-Preserving Graph Representation Learning for Bridging Local Connectivity and Role-based Similarity
Graph representation learning (GRL) methods, such as graph neural networks and graph transformer models, have been successfully used to analyze graph-structured data, mainly focusing on node classification and link predi…
Graph ClassificationGraph Representation LearningIsomorphism TestingLink Prediction+4Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-structured data. However, there are many instances where message passing can lead to over-smoothing or fail when neighboring nodes belong to different …
Node ClassificationSelf-Supervised LearningMitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning
Graph Active Learning (GAL), which aims to find the most informative nodes in graphs for annotation to maximize the Graph Neural Networks (GNNs) performance, has attracted many research efforts but remains non-trivial ch…
Active LearningDiversityNode ClassificationS-Mixup: Structural Mixup for Graph Neural Networks
Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored. In this paper, we propose a novel mixup augmentat…
ClassificationGraph ClassificationGraph Neural NetworkNode ClassificationIndependent Distribution Regularization for Private Graph Embedding
Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data publishing benefiting various downstream…
AttributeGraph EmbeddingGraph MiningLink Prediction+3Towards Temporal Edge Regression: A Case Study on Agriculture Trade Between Nations
Recently, Graph Neural Networks (GNNs) have shown promising performance in tasks on dynamic graphs such as node classification, link prediction and graph regression. However, few work has studied the temporal edge regres…
Graph RegressionLink PredictionNode ClassificationregressionKMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and crucial task in graph data analysis. This task aims to predict nodes from unseen classes which are unobserved in the training process. Existing work …
Node ClassificationRepresentation LearningTransfer LearningSR-HGN: semantic- and relation-aware heterogeneous graph neural network
Graph Neural Networks (GNNs) have received considerable attention in recent years due to their unique ability to model both topologies and semantics in the graphs. In this paper, we explore the use of GNNs in learning lo…
Graph Neural NetworkNode ClassificationNode ClusteringRelationSimMatchV2: Semi-Supervised Learning with Graph Consistency
Semi-Supervised image classification is one of the most fundamental problem in computer vision, which significantly reduces the need for human labor. In this paper, we introduce a new semi-supervised learning algorithm -…
image-classificationImage ClassificationNode ClassificationSemi-Supervised Image ClassificationNode Embedding for Homophilous Graphs with ARGEW: Augmentation of Random walks by Graph Edge Weights
Representing nodes in a network as dense vectors node embeddings is important for understanding a given network and solving many downstream tasks. In particular, for weighted homophilous graphs where similar nodes are co…
Node Classification$\mathcal{G}^2Pxy$: Generative Open-Set Node Classification on Graphs with Proxy Unknowns
Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training.…
ClassificationInductive LearningNode Classificationopen-set classification+1Local Structure-aware Graph Contrastive Representation Learning
Traditional Graph Neural Network (GNN), as a graph representation learning method, is constrained by label information. However, Graph Contrastive Learning (GCL) methods, which tackle the label problem effectively, mainl…
Contrastive LearningGraph Neural NetworkGraph Representation LearningLink Prediction+2Communication-Free Distributed GNN Training with Vertex Cut
Training Graph Neural Networks (GNNs) on real-world graphs consisting of billions of nodes and edges is quite challenging, primarily due to the substantial memory needed to store the graph and its intermediate node and e…
GPUNode ClassificationLabel Inference Attacks against Node-level Vertical Federated GNNs
Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve the models' privacy, security, and scalabi…
Federated LearningNode ClassificationVertical Federated LearningUniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification
Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Ne…
Node ClassificationRepresentation LearningSimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) unsupervised feature extraction and (ii) su…
Feature EngineeringGraph LearningGraph Representation LearningLink Prediction+3DiffusAL: Coupling Active Learning with Graph Diffusion for Label-Efficient Node Classification
Node classification is one of the core tasks on attributed graphs, but successful graph learning solutions require sufficiently labeled data. To keep annotation costs low, active graph learning focuses on selecting the m…
Active LearningGraph LearningNode Classification