Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
ALINC: Active Learning for Inductive Node Classification via Graph Sampling
Active learning (AL) for node classification typically focuses on selecting the most informative nodes for annotation within one or a few large graphs (e.g., in social network analysis). However, in other domains, such a…
Node ClassificationActive LearningLearning Long Range Spatio-Temporal Representations over Continuous Time Dynamic Graphs with State Space Models
Continuous-time dynamic graphs (CTDGs) provide a richer framework to capture fine-grained temporal patterns in evolving relational data. Long-range information propagation is a key challenge while learning representation…
Dynamic Link PredictionNode ClassificationSpatial ReasoningGJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks
Graph Neural Networks (GNNs) are vulnerable to adversarial attacks, which inherently invert connectivity patterns by introducing disassortative edges in assortative graphs and assortative edges in disassortative graphs. …
Node ClassificationGraphARC: A Comprehensive Benchmark for Graph-Based Abstract Reasoning
Relational reasoning lies at the heart of intelligence, but existing benchmarks are typically confined to formats such as grids or text. We introduce GraphARC, a benchmark for abstract reasoning on graph-structured data.…
Relational ReasoningNode ClassificationGraph GenerationLink PredictionDG-CoLearn: An Efficient Collaborative Learning Framework for Dynamic Graphs
Dynamic graph learning (DGL) is essential for modelling evolving graph data, but existing methods suffer from significant computational overhead due to repeated full-snapshot retraining and are not well-suited for collab…
Node ClassificationLink PredictionGraph LearningKG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
Large language models (LLMs) are increasingly used for knowledge base question answering (KBQA), where answering requires selecting entities from a question-specific knowledge-graph subgraph. Yet LLMs are known to halluc…
Knowledge Base Question AnsweringNode ClassificationForget Less, Generalize More: Unifying Temporal and Structural Adaptation for Dynamic Graphs
Representation learning on dynamic graphs requires capturing complex dependencies that evolve across both time and structure. Existing approaches typically adopt fixed temporal decay schemes or predetermined structural p…
Representation LearningNode ClassificationLink PredictionWhere LLM Annotators Fail: Label-Free Learning on Graphs with LLMs
Node classification on graphs often requires labeled nodes, yet obtaining labels at graph scale is expensive. When node attributes contain semantic content, such as paper abstracts, web pages, or product descriptions, la…
Node ClassificationNoise EstimationGraph LearningLearning Dynamic Graph Representations through Timespan View Contrasts
The rich information underlying graphs has inspired further investigation of unsupervised graph representation. Existing studies mainly depend on node features and topological properties within static graphs to create se…
Graph Anomaly DetectionContrastive LearningNode ClassificationARMA-C3: A Contrastive ARMA Convolutional Framework for Unsupervised and Semi-supervised Classification
In biomedical and neurodegenerative disorders, accurate and early disease identification remains challenging due to the scarcity of labeled data and the complexity of imaging patterns. To address these challenges, we int…
Representation LearningBinary ClassificationContrastive LearningNode ClassificationClosed-Form Node Classification with Exact Graph Unlearning
Graph neural networks for node classification are typically trained by gradient descent over hundreds or thousands of epochs. Recent work has shown that, when properly tuned, classic GCN/SAGE/GAT architectures can match …
Node ClassificationGraph Transductive Sharpening: Leveraging Unlabeled Predictions in Node Classification
In the transductive setting, where the full graph is observed but node labels are only partially available, progress in semi-supervised node classification has largely focused on architectural innovation. In this paper, …
Node ClassificationInstance Discrimination for Link Prediction
Recently, instance discrimination models have emerged as a major solution for self-supervised learning. Having already demonstrated its effectiveness in the image domain, instance discrimination learning is now proving e…
Self-Supervised LearningNode ClassificationLink PredictionWhat Information Matters? Graph Out-of-Distribution Detection via Tri-Component Information Decomposition
Graph neural networks are widely used for node classification, but they remain vulnerable to out-of-distribution (OOD) shifts in node features and graph structure. Prior work established that methods trained with standar…
Out-of-Distribution DetectionNode ClassificationDecoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs from different domains pose fundamental cha…
Node ClassificationDomain AdaptationLink PredictionGraph LearningThe WidthWall: A Strict Expressivity Hierarchy for Hypergraph Neural Networks
Hypergraphs provide a natural framework to model higher-order interactions in scientific, social, and biological systems. Hypergraph neural networks (HGNNs) aim to learn from such data, yet it remains unclear which highe…
Node ClassificationModeling Heterophily in Multiplex Graphs: An Adaptive Approach for Node Classification
Existing multiplex graph models often assume homophily, where connected nodes tend to belong to the same class or share similar attributes. Consequently, these models may struggle with graphs exhibiting heterophily, wher…
Node ClassificationTopoU-Net: a U-Net architecture for topological domains
Many modern datasets mix points, edges, regions, groups, objects, events, hyperedges, and relations. Yet neural architectures often force such data into grids, graphs, or sequences, obscuring higher-order structure and m…
Graph ClassificationImage ReconstructionNode ClassificationRAwR: Role-Aware Rewiring via Approximate Equitable Partition
While Graph Neural Networks (GNNs) have demonstrated significant efficacy in node classification tasks, where predictions rely on local neighborhood information, the performance of GNNs often drops when prediction tasks …
Node ClassificationBilevel Graph Structure Learning, Revisited: Inner-Channel Origins of the Reported Gain
Bilevel graph structure learning is widely understood to improve graph neural networks by jointly optimizing model parameters and a learned graph structure, with the resulting performance gain attributed to the rewired a…
Graph structure learningNode Classification