Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in ed…
Graph LearningLink PredictionNode ClassificationNormalize Then Propagate: Efficient Homophilous Regularization for Few-shot Semi-Supervised Node Classification
Graph Neural Networks (GNNs) have demonstrated remarkable ability in semi-supervised node classification. However, most existing GNNs rely heavily on a large amount of labeled data for training, which is labor-intensive …
Node ClassificationEnhancing Graph Representation Learning with Localized Topological Features
Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. …
Graph LearningGraph Representation LearningLink PredictionNode Classification+1Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive applications effectively. Temporal graph…
Abstention PredictionDynamic Link PredictionDynamic Node ClassificationGraph Learning+3DeltaGNN: Graph Neural Network with Information Flow Control
Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node cla…
Graph Neural NetworkNode ClassificationCoupled Hierarchical Structure Learning using Tree-Wasserstein Distance
In many applications, both data samples and features have underlying hierarchical structures. However, existing methods for learning these latent structures typically focus on either samples or features, ignoring possibl…
Link PredictionNode ClassificationGraph-Based Multimodal and Multi-view Alignment for Keystep Recognition
Egocentric videos capture scenes from a wearer's viewpoint, resulting in dynamic backgrounds, frequent motion, and occlusions, posing challenges to accurate keystep recognition. We propose a flexible graph-learning frame…
Graph LearningNode ClassificationEnhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training
Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However, lacking of rigorous uncertainty estimations limits their applicati…
Conformal PredictionNode ClassificationPredictionDiffGraph: Heterogeneous Graph Diffusion Model
Recent advances in Graph Neural Networks (GNNs) have revolutionized graph-structured data modeling, yet traditional GNNs struggle with complex heterogeneous structures prevalent in real-world scenarios. Despite progress …
DenoisingGraph GenerationLink Predictionmodel+1A Probabilistic Model for Node Classification in Directed Graphs
In this work, we present a probabilistic model for directed graphs where nodes have attributes and labels. This model serves as a generative classifier capable of predicting the labels of unseen nodes using either maximu…
MathNode ClassificationOvercoming Class Imbalance: Unified GNN Learning with Structural and Semantic Connectivity Representations
Class imbalance is pervasive in real-world graph datasets, where the majority of annotated nodes belong to a small set of classes (majority classes), leaving many other classes (minority classes) with only a handful of l…
Graph LearningGraph Neural NetworkNode ClassificationPseudo LabelAttention-Driven Metapath Encoding in Heterogeneous Graphs
One of the emerging techniques in node classification in heterogeneous graphs is to restrict message aggregation to pre-defined, semantically meaningful structures called metapaths. This work is the first attempt to inco…
Node ClassificationHypergraph-Based Dynamic Graph Node Classification
Node classification on static graphs has achieved significant success, but achieving accurate node classification on dynamic graphs where node topology, attributes, and labels change over time has not been well addressed…
ClassificationNode ClassificationLarge Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through th…
Community DetectionGraph LearningGraph MiningLink Prediction+1Data-Driven Self-Supervised Graph Representation Learning
Self-supervised graph representation learning (SSGRL) is a representation learning paradigm used to reduce or avoid manual labeling. An essential part of SSGRL is graph data augmentation. Existing methods usually rely on…
Data AugmentationDocument ClassificationGraph Property PredictionGraph Representation Learning+3LASE: Learned Adjacency Spectral Embeddings
We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method and leveraging the principle of algorit…
Graph AttentionGraph Neural NetworkGraph Representation LearningLink Prediction+2GraphAgent: Agentic Graph Language Assistant
Real-world data is represented in both structured (e.g., graph connections) and unstructured (e.g., textual, visual information) formats, encompassing complex relationships that include explicit links (such as social con…
Knowledge GraphsNode ClassificationTask PlanningText GenerationFedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks
Federated training methods have gained popularity for graph learning with applications including friendship graphs of social media sites and customer-merchant interaction graphs of huge online marketplaces. However, priv…
Graph AttentionGraph LearningNode ClassificationPrivacy PreservingGraph Attention is Not Always Beneficial: A Theoretical Analysis of Graph Attention Mechanisms via Contextual Stochastic Block Models
Despite the growing popularity of graph attention mechanisms, their theoretical understanding remains limited. This paper aims to explore the conditions under which these mechanisms are effective in node classification t…
Graph AttentionNode ClassificationGraph Structure Refinement with Energy-based Contrastive Learning
Graph Neural Networks (GNNs) have recently gained widespread attention as a successful tool for analyzing graph-structured data. However, imperfect graph structure with noisy links lacks enough robustness and may damage …
Contrastive LearningNode Classification