Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Gumbel-MPNN: Graph Rewiring with Gumbel-Softmax
Graph homophily has been considered an essential property for message-passing neural networks (MPNN) in node classification. Recent findings suggest that performance is more closely tied to the consistency of neighborhoo…
Node ClassificationCITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials
Text-attributed graphs(TAGs) are pervasive in real-world systems,where each node carries its own textual features. In many cases these graphs are inherently heterogeneous, containing multiple node types and diverse edge …
Representation LearningNode ClassificationSVDformer: Direction-Aware Spectral Graph Embedding Learning via SVD and Transformer
Directed graphs are widely used to model asymmetric relationships in real-world systems. However, existing directed graph neural networks often struggle to jointly capture directional semantics and global structural patt…
Graph Representation LearningNode ClassificationGraph EmbeddingPredicting the Performance of Graph Convolutional Networks with Spectral Properties of the Graph Laplacian
A common observation in the Graph Convolutional Network (GCN) literature is that stacking GCN layers may or may not result in better performance on tasks like node classification and edge prediction. We have found empiri…
Node ClassificationTransfer LearningImplicit Hypergraph Neural Network
Hypergraphs offer a generalized framework for capturing high-order relationships between entities and have been widely applied in various domains, including healthcare, social networks, and bioinformatics. Hypergraph neu…
Node ClassificationGraph Neural Diffusion via Generalized Opinion Dynamics
There has been a growing interest in developing diffusion-based Graph Neural Networks (GNNs), building on the connections between message passing mechanisms in GNNs and physical diffusion processes. However, existing met…
Node ClassificationEnhancing Fairness in Autoencoders for Node-Level Graph Anomaly Detection
Graph anomaly detection (GAD) has become an increasingly important task across various domains. With the rapid development of graph neural networks (GNNs), GAD methods have achieved significant performance improvements. …
Graph Anomaly DetectionNode ClassificationChi-Geometry: A Library for Benchmarking Chirality Prediction of GNNs
We introduce Chi-Geometry - a library that generates graph data for testing and benchmarking GNNs' ability to predict chirality. Chi-Geometry generates synthetic graph samples with (i) user-specified geometric and topolo…
Node ClassificationOver-Squashing in GNNs and Causal Inference of Rewiring Strategies
Graph neural networks (GNNs) have exhibited state-of-the-art performance across wide-range of domains such as recommender systems, material design, and drug repurposing. Yet message-passing GNNs suffer from over-squashin…
Graph ClassificationNode ClassificationCausal InferenceExact Verification of Graph Neural Networks with Incremental Constraint Solving
Graph neural networks (GNNs) are increasingly often employed in high-stakes applications, such as fraud detection or healthcare, but are susceptible to adversarial attacks. A number of techniques have been proposed to pr…
Adversarial RobustnessGraph ClassificationNode ClassificationFraud DetectionDiscrete Diffusion-Based Model-Level Explanation of Heterogeneous GNNs with Node Features
Many real-world datasets, such as citation networks, social networks, and molecular structures, are naturally represented as heterogeneous graphs, where nodes belong to different types and have additional features. For e…
Molecular Property PredictionFake News DetectionNode ClassificationHypergraph Neural Network with State Space Models for Node Classification
In recent years, graph neural networks (GNNs) have gained significant attention for node classification tasks on graph-structured data. However, traditional GNNs primarily focus on adjacency relationships between nodes, …
Node ClassificationTransferring Social Network Knowledge from Multiple GNN Teachers to Kolmogorov-Arnold Networks
Graph Neural Networks (GNNs) have shown strong performance on graph-structured data, but their reliance on graph connectivity often limits scalability and efficiency. Kolmogorov-Arnold Networks (KANs), a recent architect…
Node ClassificationGLIDR: Graph-Like Inductive Logic Programming with Differentiable Reasoning
Differentiable inductive logic programming (ILP) techniques have proven effective at finding approximate rule-based solutions to link prediction and node classification problems on knowledge graphs; however, the common a…
Inductive logic programmingKnowledge Graph CompletionNode ClassificationKnowledge GraphsAn Effective Approach for Node Classification in Textual Graphs
Textual Attribute Graphs (TAGs) are critical for modeling complex networks like citation networks, but effective node classification remains challenging due to difficulties in integrating rich semantics from text with st…
Node ClassificationA Scalable Pretraining Framework for Link Prediction with Efficient Adaptation
Link Prediction (LP) is a critical task in graph machine learning. While Graph Neural Networks (GNNs) have significantly advanced LP performance recently, existing methods face key challenges including limited supervisio…
Node ClassificationLink PredictionAdversarial Attacks and Defenses on Graph-aware Large Language Models (LLMs)
Large Language Models (LLMs) are increasingly integrated with graph-structured data for tasks like node classification, a domain traditionally dominated by Graph Neural Networks (GNNs). While this integration leverages r…
Node ClassificationAdversarial AttackOldie but Goodie: Re-illuminating Label Propagation on Graphs with Partially Observed Features
In real-world graphs, we often encounter missing feature situations where a few or the majority of node features, e.g., sensitive information, are missed. In such scenarios, directly utilizing Graph Neural Networks (GNNs…
Contrastive LearningNode ClassificationNeighbor-Sampling Based Momentum Stochastic Methods for Training Graph Neural Networks
Graph convolutional networks (GCNs) are a powerful tool for graph representation learning. Due to the recursive neighborhood aggregations employed by GCNs, efficient training methods suffer from a lack of theoretical gua…
Graph Representation LearningNode ClassificationSheaf Graph Neural Networks via PAC-Bayes Spectral Optimization
Over-smoothing in Graph Neural Networks (GNNs) causes collapse in distinct node features, particularly on heterophilic graphs where adjacent nodes often have dissimilar labels. Although sheaf neural networks partially mi…
Node Classification