Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
AgentGL: Towards Agentic Graph Learning with LLMs via Reinforcement Learning
Large Language Models (LLMs) increasingly rely on agentic capabilities-iterative retrieval, tool use, and decision-making-to overcome the limits of static, parametric knowledge. Yet existing agentic frameworks treat exte…
Reinforcement LearningNode ClassificationLink PredictionGraph LearningAnalytic Drift Resister for Non-Exemplar Continual Graph Learning
Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitig…
class-incremental learningNode ClassificationContinual LearningGraph LearningEfficient and Scalable Granular-ball Graph Coarsening Method for Large-scale Graph Node Classification
Graph Convolutional Network (GCN) is a model that can effectively handle graph data tasks and has been successfully applied. However, for large-scale graph datasets, GCN still faces the challenge of high computational ov…
Node ClassificationCGRL: Causal-Guided Representation Learning for Graph Out-of-Distribution Generalization
Graph Neural Networks (GNNs) have achieved impressive performance in graph-related tasks. However, they suffer from poor generalization on out-of-distribution (OOD) data, as they tend to learn spurious correlations. Such…
Representation LearningNode ClassificationMulti-view Graph Convolutional Network with Fully Leveraging Consistency via Granular-ball-based Topology Construction, Feature Enhancement and Interactive Fusion
The effective utilization of consistency is crucial for multi-view learning. GCNs leverage node connections to propagate information across the graph, facilitating the exploitation of consistency in multi-view data. Howe…
Node ClassificationLLM-Enhanced Energy Contrastive Learning for Out-of-Distribution Detection in Text-Attributed Graphs
Text-attributed graphs, where nodes are enriched with textual attributes, have become a powerful tool for modeling real-world networks such as citation, social, and transaction networks. However, existing methods for lea…
Out-of-Distribution DetectionContrastive LearningNode ClassificationPosition: Spectral GNNs Are Neither Spectral Nor Superior for Node Classification
Spectral Graph Neural Networks (Spectral GNNs) for node classification promise frequency-domain filtering on graphs, yet rest on flawed foundations. Recent work shows that graph Laplacian eigenvectors do not in general h…
Node ClassificationA Depth-Aware Comparative Study of Euclidean and Hyperbolic Graph Neural Networks on Bitcoin Transaction Systems
Bitcoin transaction networks are large scale socio- technical systems in which activities are represented through multi-hop interaction patterns. Graph Neural Networks(GNNs) have become a widely adopted tool for analyzin…
Node ClassificationFraud DetectionGaLoRA: Parameter-Efficient Graph-Aware LLMs for Node Classification
The rapid rise of large language models (LLMs) and their ability to capture semantic relationships has led to their adoption in a wide range of applications. Text-attributed graphs (TAGs) are a notable example where LLMs…
Recommendation SystemsNode Classification$P^2$GNN: Two Prototype Sets to boost GNN Performance
Message Passing Graph Neural Networks (MP-GNNs) have garnered attention for addressing various industry challenges, such as user recommendation and fraud detection. However, they face two major hurdles: (1) heavy relianc…
Node ClassificationFraud DetectionTransductive Generalization via Optimal Transport and Its Application to Graph Node Classification
Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establish new representation-based generalizatio…
Node ClassificationMultimodal Graph Representation Learning with Dynamic Information Pathways
Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message pa…
Graph Representation LearningNode ClassificationLink PredictionGraph LearningNot All Neighbors Matter: Understanding the Impact of Graph Sparsification on GNN Pipelines
As graphs scale to billions of nodes and edges, graph Machine Learning workloads are constrained by the cost of multi-hop traversals over exponentially growing neighborhoods. While various system-level and algorithmic op…
Graph Neural NetworkNode ClassificationMario: Multimodal Graph Reasoning with Large Language Models
Recent advances in large language models (LLMs) have opened new avenues for multimodal reasoning. Yet, most existing methods still rely on pretrained vision-language models (VLMs) to encode image-text pairs in isolation,…
Multimodal ReasoningContrastive LearningNode ClassificationLink PredictionGraph Hopfield Networks: Energy-Based Node Classification with Associative Memory
We introduce Graph Hopfield Networks, whose energy function couples associative memory retrieval with graph Laplacian smoothing for node classification. Gradient descent on this joint energy yields an iterative update in…
Node ClassificationInvariant-Stratified Propagation for Expressive Graph Neural Networks
Graph Neural Networks (GNNs) face fundamental limitations in expressivity and capturing structural heterogeneity. Standard message-passing architectures are constrained by the 1-dimensional Weisfeiler-Leman (1-WL) test, …
Graph ClassificationNode ClassificationDynamicGTR: Leveraging Graph Topology Representation Preferences to Boost VLM Capabilities on Graph QAs
Vision-Language Models (VLMs) have emerged as versatile solutions for zero-shot question answering (QA) across various domains. However, enabling VLMs to effectively comprehend structured graphs and perform accurate, eff…
Node ClassificationQuestion AnsweringLink PredictionVecFormer: Towards Efficient and Generalizable Graph Transformer with Graph Token Attention
Graph Transformer has demonstrated impressive capabilities in the field of graph representation learning. However, existing approaches face two critical challenges: (1) most models suffer from exponentially increasing co…
Graph Representation LearningNode ClassificationHyperbolic Busemann Neural Networks
Hyperbolic spaces provide a natural geometry for representing hierarchical and tree-structured data due to their exponential volume growth. To leverage these benefits, neural networks require intrinsic and efficient comp…
Image ClassificationNode ClassificationLink PredictionHEHRGNN: A Unified Embedding Model for Knowledge Graphs with Hyperedges and Hyper-Relational Edges
Knowledge Graph(KG) has gained traction as a machine-readable organization of real-world knowledge for analytics using artificial intelligence systems. Graph Neural Network(GNN), is proven to be an effective KG embedding…
Graph ClassificationGraph Neural NetworkNode ClassificationKnowledge Graphs