Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Let the Heads Talk: Beyond Diagonal Graph Attention
Sheaf Neural Networks generalize scalar-weighted message passing by replacing scalar edge weights with linear transport maps between local feature spaces. Yet the role of this matrix-valued transport is entangled with th…
Relational ReasoningNode ClassificationGraph LearningA Comparative Study of Counterfactual Explainers for Graph Neural Networks Enabling Multiple Types of Graph Edit
Counterfactual explanations for graph-structured data seek to determine minimal and realistic modifications required in an input graph to alter a model's prediction to a predefined output. Although counterfactual explain…
Node ClassificationPaSta: Noisy Node Classification with Partial Label Learning
Noisy node classification problem is a fundamental yet challenging task for real-world graph-related web services, where node labels are often corrupted or unreliable due to weak supervision or automatic annotation. Howe…
Partial Label LearningNode ClassificationReCoG: Reciprocal Co-Evolution for Multimodal Graph Learning
Multimodal graph learning requires jointly training over graph structure and heterogeneous node attributes, yet existing methods largely decouple these processes: prior multimodal graph neural networks (GNNs) focus on al…
Graph structure learningRepresentation LearningNode ClassificationLink PredictionFairness-Aware Network Embeddings: Methods, Applications, and Challenges
Network embedding methods learn low-dimensional representations of graph-structured data to support downstream tasks such as node classification, link prediction, and influence maximization. However, real-world networks …
Representation LearningGraph Neural NetworkNode ClassificationLink PredictionDynamic Graph Prompting via Topology-Routed Mixed-Curvature Experts
Dynamic graph prompting freezes a pre-trained temporal backbone and adapts it to label-scarce downstream tasks using lightweight prompts. However, existing methods operate within a single, fixed embedding space. In this …
Node ClassificationLink PredictionNonlinear Laplacians Improve Signed-Directed Graph Learning
While signed-directed graphs have been studied using linear Laplacians in the design of graph neural networks, relatively little research has focused on developing non-linear Laplacian operators for such networks. We int…
Node ClassificationLink PredictionGraph LearningSame Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node classification (NC) and link prediction (LP). However, existing evalua…
Node ClassificationLink PredictionNo Data Is Not No Risk: Visibility Aware Graph-Based Inference of Business Conduct Risk
The monitoring of business conduct risk is hindered by sparse, uneven, and visibility-biased data. Prior studies show that business conduct risk information and media coverage propagate through supply chain, peer, and co…
Node ClassificationTSAI-MetaFraud: A Benchmark Dataset for Financial Fraud Transaction and Behavioral Risk Detection in Metaverse Ecosystems
The emergence of metaverse platforms has created virtual economies that introduce new challenges related to fraud, bot activity, and illicit financial behavior. Despite growing interest in trustworthy metaverse analytics…
Node ClassificationLink PredictionFraud DetectionEdgeRefine: Privacy-Utility Balance for Graphs via Jaccard Sampling under Edge Differential Privacy
Graph Neural Networks (GNNs) have shown considerable success in learning from graph-structured data, but their use in privacy-sensitive areas remains difficult because graph structure can leak sensitive link information.…
Graph ClassificationNode ClassificationGraph LearningMulti-Label Node Classification with Label Influence Propagation
Graphs are a complex and versatile data structure used across various domains, with possibly multi-label nodes playing a particularly crucial role. Examples include proteins in PPI networks with multiple functions and us…
Node ClassificationTAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often handle the two modalities separately: graph …
Node ClassificationLink PredictionGraph LearningLearning Graphs through Continuous Information Entropy Fields
Graph theory is inherently descriptive, capturing what relationships exist but not why they arise, because it treats edges as primitive constructs. This paper proposes a new explanatory framework for graph learning, wher…
Graph ClassificationNode ClassificationGraph LearningBoundary Embedding Shaping with Adaptive Contrastive Learning for Graph Structural Disentanglement
Graph neural networks (GNNs) excel at aggregating neighbor information for classification, yet their performance is hindered by graph structural entanglement, where spurious correlations from semantically irrelevant neig…
Contrastive LearningNode ClassificationLink PredictionSEAGAN: domain-Specific and Edge-Aware Graph Attention Network for Dynamic Plant Processes
Graph neural networks (GNNs) offer a flexible framework for learning from scientific data with physical, biological, or functional associations. One promising domain is plant physiology, where observed responses result f…
Node ClassificationWhen the Tool Decides: LLM Agents Defer Blindly to Graph Neural Network Tools, and Stronger Backbones Defer More
A growing line of work equips large language model (LLM) agents with graph neural networks (GNNs) as callable tools, assuming the agent exercises judgment over when and how much to rely on such a tool. We test this direc…
Graph Neural NetworkNode ClassificationGraphInfer-Bench: Benchmarking LLM's Inference Capability on Graphs
Graph analysis underlies many applications whose answers cannot be looked up in a single record or retrieved along a path: laundering rings, drug repurposing, user preference, and scientific theme are all inferred from a…
Node ClassificationCommunity DetectionOutlier DetectionOn solving symmetric multi-type orthogonal non-negative matrix tri-factorization problem
We study the symmetric multi-type orthogonal non-negative matrix tri-factorization problem, where several symmetric non-negative matrices are simultaneously approximated by factors of the form $GS_{i}G^{\top}$, with a sh…
Node ClassificationLink PredictionGraph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node classification in homophilous graphs, where nodes with the same class label…
Graph Neural NetworkNode Classification