Papers Node Classification
“Node Classification” 태그가 달린 논문 2,077편 · 필터 해제
Spatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Account Takeover (ATO) fraud poses a significant challenge in consumer banking, requiring high recall under strict latency while minimizing friction for legitimate users. Production systems typically rely on tabular grad…
Node ClassificationFraud DetectionGraph LearningActions Speak Louder than Prompts: A Large-Scale Study of LLMs for Graph Inference
Large language models (LLMs) are increasingly used for text-rich graph machine learning tasks such as node classification in high-impact domains like fraud detection and recommendation systems. Yet, despite a surge of in…
Recommendation SystemsNode ClassificationFraud DetectionCode GenerationBridging Computational Social Science and Deep Learning: Cultural Dissemination-Inspired Graph Neural Networks
Graph Neural Networks (GNNs) have become vital in applications like document classification in citation networks, epidemic forecasting, viral marketing, user recommendation in social networks, and network monitoring. How…
Computational EfficiencyDocument ClassificationNode ClassificationGnnXemplar: Exemplars to Explanations -- Natural Language Rules for Global GNN Interpretability
Graph Neural Networks (GNNs) are widely used for node classification, yet their opaque decision-making limits trust and adoption. While local explanations offer insights into individual predictions, global explanation me…
Node ClassificationGraphDerm: Fusing Imaging, Physical Scale, and Metadata in a Population-Graph Classifier for Dermoscopic Lesions
Introduction. Dermoscopy aids melanoma triage, yet image-only AI often ignores patient metadata (age, sex, site) and the physical scale needed for geometric analysis. We present GraphDerm, a population-graph framework th…
Lesion SegmentationNode ClassificationMoSE: Unveiling Structural Patterns in Graphs via Mixture of Subgraph Experts
While graph neural networks (GNNs) have achieved great success in learning from graph-structured data, their reliance on local, pairwise message passing restricts their ability to capture complex, high-order subgraph pat…
Representation LearningNode ClassificationThe Role of Community Detection Methods in Performance Variations of Graph Mining Tasks
In real-world scenarios, large graphs represent relationships among entities in complex systems. Mining these large graphs often containing millions of nodes and edges helps uncover structural patterns and meaningful ins…
Node ClassificationCommunity DetectionLink PredictionGraph-based Integrated Gradients for Explaining Graph Neural Networks
Integrated Gradients (IG) is a common explainability technique to address the black-box problem of neural networks. Integrated gradients assumes continuous data. Graphs are discrete structures making IG ill-suited to gra…
Node ClassificationPAC-Bayesian Generalization Bounds for Graph Convolutional Networks on Inductive Node Classification
Graph neural networks (GNNs) have achieved remarkable success in processing graph-structured data across various applications. A critical aspect of real-world graphs is their dynamic nature, where new nodes are continual…
Node ClassificationLearning from one graph: transductive learning guarantees via the geometry of small random worlds
Since their introduction by Kipf and Welling in $2017$, a primary use of graph convolutional networks is transductive node classification, where missing labels are inferred within a single observed graph and its feature …
Node ClassificationBringing Graphs to the Table: Zero-shot Node Classification via Tabular Foundation Models
Graph foundation models (GFMs) have recently emerged as a promising paradigm for achieving broad generalization across various graph data. However, existing GFMs are often trained on datasets that may not fully reflect r…
Time Series ForecastingNode ClassificationGraph LearningADMP-GNN: Adaptive Depth Message Passing GNN
Graph Neural Networks (GNNs) have proven to be highly effective in various graph learning tasks. A key characteristic of GNNs is their use of a fixed number of message-passing steps for all nodes in the graph, regardless…
Node ClassificationGraph LearningFlow Matters: Directional and Expressive GNNs for Heterophilic Graphs
In heterophilic graphs, where neighboring nodes often belong to different classes, conventional Graph Neural Networks (GNNs) struggle due to their reliance on local homophilous neighborhoods. Prior studies suggest that m…
Node ClassificationGraph LearningLocal Virtual Nodes for Alleviating Over-Squashing in Graph Neural Networks
Over-squashing is a challenge in training graph neural networks for tasks involving long-range dependencies. In such tasks, a GNN's receptive field should be large enough to enable communication between distant nodes. Ho…
Node ClassificationParameter-Free Structural-Diversity Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown remarkable performance in structured data modeling tasks such as node classification. However, mainstream approaches generally rely on a large number of trainable parameters and fi…
Graph Representation LearningGraph Neural NetworkNode ClassificationBounds on Perfect Node Classification: A Convex Graph Clustering Perspective
We present an analysis of the transductive node classification problem, where the underlying graph consists of communities that agree with the node labels and node features. For node classification, we propose a novel op…
Node ClassificationGraph ClusteringDynamic Triangulation-Based Graph Rewiring for Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as the leading paradigm for learning over graph-structured data. However, their performance is limited by issues inherent to graph topology, most notably oversquashing and oversm…
Node ClassificationMemorization in Graph Neural Networks
Deep neural networks (DNNs) have been shown to memorize their training data, yet similar analyses for graph neural networks (GNNs) remain largely under-explored. We introduce NCMemo (Node Classification Memorization), th…
Node ClassificationLimits of message passing for node classification: How class-bottlenecks restrict signal-to-noise ratio
Message passing neural networks (MPNNs) are powerful models for node classification but suffer from performance limitations under heterophily (low same-class connectivity) and structural bottlenecks in the graph. We prov…
Node ClassificationGraph-R1: Incentivizing the Zero-Shot Graph Learning Capability in LLMs via Explicit Reasoning
Generalizing to unseen graph tasks without task-pecific supervision remains challenging. Graph Neural Networks (GNNs) are limited by fixed label spaces, while Large Language Models (LLMs) lack structural inductive biases…
Reinforcement LearningGraph ClassificationNode ClassificationLink Prediction