Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Feature Transportation Improves Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable success in learning representations for graph-structured data. However, GNNs still face challenges in modeling complex phenomena that involve feature transportation. In …
Node ClassificationMUSE: Multi-View Contrastive Learning for Heterophilic Graphs
In recent years, self-supervised learning has emerged as a promising approach in addressing the issues of label dependency and poor generalization performance in traditional GNNs. However, existing self-supervised method…
Contrastive LearningDiversityNode ClassificationRepresentation Learning+1Addressing the Impact of Localized Training Data in Graph Neural Networks
Graph Neural Networks (GNNs) have achieved notable success in learning from graph-structured data, owing to their ability to capture intricate dependencies and relationships between nodes. They excel in various applicati…
Anomaly DetectionGraph GenerationLink PredictionNode Classification+1OpenGDA: Graph Domain Adaptation Benchmark for Cross-network Learning
Graph domain adaptation models are widely adopted in cross-network learning tasks, with the aim of transferring labeling or structural knowledge. Currently, there mainly exist two limitations in evaluating graph domain a…
Domain AdaptationGRAPH DOMAIN ADAPTATIONNode ClassificationLearning Adaptive Neighborhoods for Graph Neural Networks
Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or l…
Node ClassificationPoint Cloud ClassificationTrajectory PredictionLong-range Meta-path Search on Large-scale Heterogeneous Graphs
Utilizing long-range dependency, a concept extensively studied in homogeneous graphs, remains underexplored in heterogeneous graphs, especially on large ones, posing two significant challenges: Reducing computational cos…
Node ClassificationNode Property PredictionPolyGNN: Polyhedron-based Graph Neural Network for 3D Building Reconstruction from Point Clouds
We present PolyGNN, a polyhedron-based graph neural network for 3D building reconstruction from point clouds. PolyGNN learns to assemble primitives obtained by polyhedral decomposition via graph node classification, achi…
Graph Neural NetworkNode ClassificationFrom random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential…
Graph Representation LearningNode ClassificationRepresentation LearningCurriculum Learning for Graph Neural Networks: A Multiview Competence-based Approach
A curriculum is a planned sequence of learning materials and an effective one can make learning efficient and effective for both humans and machines. Recent studies developed effective data-driven curriculum learning app…
Link PredictionNode ClassificationSchedulingExamining the Effects of Degree Distribution and Homophily in Graph Learning Models
Despite a surge in interest in GNN development, homogeneity in benchmarking datasets still presents a fundamental issue to GNN research. GraphWorld is a recent solution which uses the Stochastic Block Model (SBM) to gene…
BenchmarkingGraph ClusteringGraph LearningNode Classification+1Automated Knowledge Modeling for Cancer Clinical Practice Guidelines
Clinical Practice Guidelines (CPGs) for cancer diseases evolve rapidly due to new evidence generated by active research. Currently, CPGs are primarily published in a document format that is ill-suited for managing this d…
ClassificationNode ClassificationSupervised Attention Using Homophily in Graph Neural Networks
Graph neural networks have become the standard approach for dealing with learning problems on graphs. Among the different variants of graph neural networks, graph attention networks (GATs) have been applied with great su…
Graph AttentionNode ClassificationTowards Fair Graph Neural Networks via Graph Counterfactual
Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and …
counterfactualFairnessGraph LearningNode ClassificationExploring the Potential of Large Language Models (LLMs) in Learning on Graphs
Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), …
General KnowledgeNode ClassificationLearning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a tec…
Graph LearningGraph Neural NetworkLink PredictionNode ClassificationTransformerG2G: Adaptive time-stepping for learning temporal graph embeddings using transformers
Dynamic graph embedding has emerged as a very effective technique for addressing diverse temporal graph analytic tasks (i.e., link prediction, node classification, recommender systems, anomaly detection, and graph genera…
Anomaly DetectionComputational EfficiencyDynamic graph embeddingGraph Embedding+5HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent work pointed out that simple homogeneou…
Link PredictionNode ClassificationNode ClusteringA Survey on Graph Classification and Link Prediction based on GNN
Traditional convolutional neural networks are limited to handling Euclidean space data, overlooking the vast realm of real-life scenarios represented as graph data, including transportation networks, social networks, and…
Graph ClassificationGraph Neural NetworkLink PredictionNode ClassificationRe-Think and Re-Design Graph Neural Networks in Spaces of Continuous Graph Diffusion Functionals
Graph neural networks (GNNs) are widely used in domains like social networks and biological systems. However, the locality assumption of GNNs, which limits information exchange to neighboring nodes, hampers their ability…
DenoisingGenerative Adversarial NetworkGraph EmbeddingGraph Learning+3Diffusion-Jump GNNs: Homophiliation via Learnable Metric Filters
High-order Graph Neural Networks (HO-GNNs) have been developed to infer consistent latent spaces in the heterophilic regime, where the label distribution is not correlated with the graph structure. However, most of the e…
Node Classification