Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various graph-related tasks. Conventional methods a…
Anomaly DetectionGraph Anomaly DetectionNode ClassificationStochastic Block ModelUnlearning Algorithmic Biases over Graphs
The growing enforcement of the right to be forgotten regulations has propelled recent advances in certified (graph) unlearning strategies to comply with data removal requests from deployed machine learning (ML) models. M…
FairnessNode ClassificationPave Your Own Path: Graph Gradual Domain Adaptation on Fused Gromov-Wasserstein Geodesics
Graph neural networks, despite their impressive performance, are highly vulnerable to distribution shifts on graphs. Existing graph domain adaptation (graph DA) methods often implicitly assume a \textit{mild} shift betwe…
Domain AdaptationGRAPH DOMAIN ADAPTATIONNode ClassificationIt Takes a Graph to Know a Graph: Rewiring for Homophily with a Reference Graph
Graph Neural Networks (GNNs) excel at analyzing graph-structured data but struggle on heterophilic graphs, where connected nodes often belong to different classes. While this challenge is commonly addressed with speciali…
Node ClassificationFinding Counterfactual Evidences for Node Classification
Counterfactual learning is emerging as an important paradigm, rooted in causality, which promises to alleviate common issues of graph neural networks (GNNs), such as fairness and interpretability. However, as in many rea…
ClassificationcounterfactualFairnessNode ClassificationInstance-Prototype Affinity Learning for Non-Exemplar Continual Graph Learning
Graph Neural Networks (GNN) endure catastrophic forgetting, undermining their capacity to preserve previously acquired knowledge amid the assimilation of novel information. Rehearsal-based techniques revisit historical e…
Contrastive LearningGraph LearningNode ClassificationEfficient Mixed Precision Quantization in Graph Neural Networks
Graph Neural Networks (GNNs) have become essential for handling large-scale graph applications. However, the computational demands of GNNs necessitate the development of efficient methods to accelerate inference. Mixed p…
Graph ClassificationNode ClassificationQuantizationExploiting Text Semantics for Few and Zero Shot Node Classification on Text-attributed Graph
Text-attributed graph (TAG) provides a text description for each graph node, and few- and zero-shot node classification on TAGs have many applications in fields such as academia and social networks. Existing work utilize…
Node ClassificationTAGSpecSphere: Dual-Pass Spectral-Spatial Graph Neural Networks with Certified Robustness
We introduce SpecSphere, the first dual-pass spectral-spatial GNN that certifies every prediction against both $\ell\_{0}$ edge flips and $\ell\_{\infty}$ feature perturbations, adapts to the full homophily-heterophily s…
Node ClassificationRepresentation Learning with Mutual Influence of Modalities for Node Classification in Multi-Modal Heterogeneous Networks
Nowadays, numerous online platforms can be described as multi-modal heterogeneous networks (MMHNs), such as Douban's movie networks and Amazon's product review networks. Accurately categorizing nodes within these network…
Graph Neural NetworkNode ClassificationRepresentation LearningMulti-Granular Attention based Heterogeneous Hypergraph Neural Network
Heterogeneous graph neural networks (HeteGNNs) have demonstrated strong abilities to learn node representations by effectively extracting complex structural and semantic information in heterogeneous graphs. Most of the p…
Graph Representation LearningNode ClassificationNode ClusteringRepresentation LearningWide & Deep Learning for Node Classification
Wide & Deep, a simple yet effective learning architecture for recommendation systems developed by Google, has had a significant impact in both academia and industry due to its combination of the memorization ability of g…
ClassificationDeep LearningFeature EngineeringMemorization+2Causally Fair Node Classification on Non-IID Graph Data
Fair machine learning seeks to identify and mitigate biases in predictions against unfavorable populations characterized by demographic attributes, such as race and gender. Recently, a few works have extended fairness to…
Causal InferenceFairnessNode ClassificationScalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling
Large Language Models (LLMs) have demonstrated strong capabilities in various natural language processing tasks; however, their application to graph-related problems remains limited, primarily due to scalability constrai…
Graph LearningLink PredictionNode ClassificationABG-NAS: Adaptive Bayesian Genetic Neural Architecture Search for Graph Representation Learning
Effective and efficient graph representation learning is essential for enabling critical downstream tasks, such as node classification, link prediction, and subgraph search. However, existing graph neural network (GNN) a…
Graph Neural NetworkGraph Representation LearningLink PredictionNeural Architecture Search+2FedHERO: A Federated Learning Approach for Node Classification Task on Heterophilic Graphs
Federated Graph Learning (FGL) empowers clients to collaboratively train Graph neural networks (GNNs) in a distributed manner while preserving data privacy. However, FGL methods usually require that the graph data owned …
Federated LearningGraph LearningNode ClassificationOut-of-Distribution Detection in Heterogeneous Graphs via Energy Propagation
Graph neural networks (GNNs) are proven effective in extracting complex node and structural information from graph data. While current GNNs perform well in node classification tasks within in-distribution (ID) settings, …
Node ClassificationOut-of-Distribution DetectionGraph Fourier Transformer with Structure-Frequency Information
Graph Transformers (GTs) have shown advantages in numerous graph structure tasks but their self-attention mechanism ignores the generalization bias of graphs, with existing methods mainly compensating for this bias from …
Graph ClassificationInductive BiasNode ClassificationEfficient Learning on Large Graphs using a Densifying Regularity Lemma
Learning on large graphs presents significant challenges, with traditional Message Passing Neural Networks suffering from computational and memory costs scaling linearly with the number of edges. We introduce the Interse…
Graph Neural NetworkKnowledge Graph CompletionLEMMANode ClassificationMSGCN: Multiplex Spatial Graph Convolution Network for Interlayer Link Weight Prediction
Graph Neural Networks (GNNs) have been widely used for various learning tasks, ranging from node classification to link prediction. They have demonstrated excellent performance in multiple domains involving graph-structu…
Link PredictionNode ClassificationPrediction