Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Uncertainty-Aware Robust Learning on Noisy Graphs
Graph neural networks have shown impressive capabilities in solving various graph learning tasks, particularly excelling in node classification. However, their effectiveness can be hindered by the challenges arising from…
Graph LearningGraph Neural NetworkNode ClassificationInductive Linear Probing for Few-shot Node Classification
Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting. However, the existing literature predominantly focuses on transduct…
ClassificationFew-Shot LearningMeta-LearningNode ClassificationGraph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exp…
Classificationglobal-optimizationNode ClassificationCARL-G: Clustering-Accelerated Representation Learning on Graphs
Self-supervised learning on graphs has made large strides in achieving great performance in various downstream tasks. However, many state-of-the-art methods suffer from a number of impediments, which prevent them from re…
ClusteringContrastive LearningGraph Representation LearningNode Classification+3Virtual Node Tuning for Few-shot Node Classification
Few-shot Node Classification (FSNC) is a challenge in graph representation learning where only a few labeled nodes per class are available for training. To tackle this issue, meta-learning has been proposed to transfer s…
ClassificationGraph Representation LearningMeta-LearningNode Classification+1On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks
Graph Neural Network (GNN) research has highlighted a relationship between high homophily (i.e., the tendency of nodes of the same class to connect) and strong predictive performance in node classification. However, rece…
Graph Neural NetworkNode ClassificationBeMap: Balanced Message Passing for Fair Graph Neural Network
Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplifying the bias. In this paper, we first in…
FairnessGraph Neural NetworkNode ClassificationDualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness
Graph-based semi-supervised node classification has been shown to become a state-of-the-art approach in many applications with high research value and significance. Most existing methods are only based on the original in…
MULTI-VIEW LEARNINGNode ClassificationRepresentation LearningFast and Effective GNN Training with Linearized Random Spanning Trees
We present a new effective and scalable framework for training GNNs in node classification tasks, based on the effective resistance, a powerful tool solidly rooted in graph theory. Our approach progressively refines the …
Node ClassificationSchema First! Learn Versatile Knowledge Graph Embeddings by Capturing Semantics with MASCHInE
Knowledge graph embedding models (KGEMs) have gained considerable traction in recent years. These models learn a vector representation of knowledge graph entities and relations, a.k.a. knowledge graph embeddings (KGEs). …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsLink Prediction+2Randomized Schur Complement Views for Graph Contrastive Learning
We introduce a randomized topological augmentor based on Schur complements for Graph Contrastive Learning (GCL). Given a graph laplacian matrix, the technique generates unbiased approximations of its Schur complements an…
Contrastive LearningGraph ClassificationNode ClassificationOptimal Inference in Contextual Stochastic Block Models
The contextual stochastic block model (cSBM) was proposed for unsupervised community detection on attributed graphs where both the graph and the high-dimensional node information correlate with node labels. In the contex…
Community DetectionNode ClassificationStochastic Block ModelExplaining and Adapting Graph Conditional Shift
Graph Neural Networks (GNNs) have shown remarkable performance on graph-structured data. However, recent empirical studies suggest that GNNs are very susceptible to distribution shift. There is still significant ambiguit…
Domain AdaptationGraph ClassificationNode ClassificationUnsupervised Domain AdaptationTowards Deep Attention in Graph Neural Networks: Problems and Remedies
Graph neural networks (GNNs) learn the representation of graph-structured data, and their expressiveness can be further enhanced by inferring node relations for propagation. Attention-based GNNs infer neighbor importance…
Deep AttentionGraph AttentionNode ClassificationClarify Confused Nodes via Separated Learning
Graph neural networks (GNNs) have achieved remarkable advances in graph-oriented tasks. However, real-world graphs invariably contain a certain proportion of heterophilous nodes, challenging the homophily assumption of t…
Node ClassificationDemystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and certain heterophilic graphs. Notably, m…
AllNode ClassificationPitfalls in Link Prediction with Graph Neural Networks: Understanding the Impact of Target-link Inclusion & Better Practices
While Graph Neural Networks (GNNs) are remarkably successful in a variety of high-impact applications, we demonstrate that, in link prediction, the common practices of including the edges being predicted in the graph at …
Link PredictionNode ClassificationRenormalized Graph Representations for Node Classification
Graph neural networks process information on graphs represented at a given resolution scale. We analyze the effect of using different coarse-grained graph resolutions, obtained through the Laplacian renormalization group…
ClassificationNode ClassificationSpectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials
Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.…
Graph LearningNode ClassificationNode Property PredictionvalidThere is more to graphs than meets the eye: Learning universal features with self-supervision
We study the problem of learning features through self-supervision that are generalisable to multiple graphs. State-of-the-art graph self-supervision restricts training to only one graph, resulting in graph-specific mode…
Node ClassificationRepresentation LearningSelf-Supervised Learning