Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
PRISM: Topology-Aware Cross-Modal Imputation for Modality-Deficient Federated Graph Learning
Multimodal federated graph learning (MM-FGL) aims to collaboratively learn from decentralized graphs with text and images. However, real-world clients may not share a common modality basis: a visual-search client may con…
Graph LearningAre Large Language Models Suitable for Graph Computation? Progress and Prospects
Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algorithmic operations. Yet, it remains unclear when LLMs can reliably s…
Graph LearningpolyDAG: Polynomial Acyclicity Constraints for Efficient Continuous Causal Discovery in Visual Semantic Graphs
Modern image-analysis pipelines often convert images into structured semantic variables, such as facial attributes, object concepts, and scene descriptors. Learning directed dependencies among these variables can produce…
Graph LearningCausalPOI: Spatio-Temporal Graph-Based Causal Modeling for Cold-Start POI Check-in Forecasting
As urban environments continue to evolve rapidly, accurately modeling the dynamic behaviour of Points of Interest is essential for supporting data-driven urban planning and commercial decision-making. While recent advanc…
Representation LearningGraph LearningMessage Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective
Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation meth…
Graph LearningTopology-Aware Gaussian Graph Repair for Robust Graph Neural Networks
Graph neural networks have achieved strong performance on graph-structured data, but their effectiveness depends heavily on the quality of the observed graph. In real applications, graph topology is often imperfect: nois…
Graph structure learningGraph LearningWhen Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node information into graph tokens, GLMs allow LLMs to …
Graph LearningIstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhibit limited effectiveness in real-time a…
Prompt EngineeringAnomaly DetectionGraph LearningConstruction of Historical Knowledge Graphs Based on BERT and Graph Neural Networks
Through digital humanities research and scale-up historical data analysis, a significant amount of traditional historical text is converted into structured knowledge graphs. This paper provides a high-level architecture …
Knowledge GraphsImage RetrievalGraph LearningDoing well with less! On Sampling Techniques for Empirical Pairwise Loss Estimation/Minimization
Many machine learning problems, including similarity learning, ranking, and clustering, rely on empirical pairwise loss functions whose quadratic computational cost quickly becomes prohibitive at scale. We demonstrate ho…
Graph LearningScaling Higher-Order Graph Learning with Maximal Clique Complexes
Graph neural networks (GNNs) are limited to modeling pairwise interactions, while higher-order models based on cell complexes achieve greater expressivity but often suffer from poor scalability. We introduce simplified a…
Computational EfficiencyGraph LearningDG-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 LearningModeling Spectral Energy Shifts in Spatio-Temporal Graph Anomaly Detection
Graph anomaly detection methods aim to distinguish anomalous nodes. While prior methods characterize anomalies through increased variation in the spectral energy distributions, they overlook those that result in decrease…
Graph Anomaly DetectionGraph LearningTemporal Motif-aware Graph Test-time Adaptation for OOD Blockchain Anomaly Detection
Ever-evolving transaction patterns have significantly hindered anomaly detection on emerging cryptocurrency blockchains due to the vast number of addresses and diverse anomalous behaviors. Recently, advanced Graph Anomal…
Graph Anomaly DetectionTest-time AdaptationGraph LearningWhere 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 LearningTemporal Hyperbolic Graph Representation Learning for Scale-Free Internet Routing and Delay Prediction
Predicting Internet round-trip time (RTT) is critical for routing optimization, quality-of-service (QoS) provisioning, and traffic engineering, yet remains challenging due to long-term temporal dependencies, evolving rou…
Graph Representation LearningLink PredictionGraph LearningApplications of temporal graph learning for predicting the dynamics of biological systems
Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operat…
Representation LearningLink PredictionGraph LearningClustering as Reasoning: A $k$-Means Interpretation of Chain-of-Thought Graph Learning
Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) on text-attributed graphs (TAGs). This work reframes CoT-based graph learning through the princip…
Graph Representation LearningGraph LearningMedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
Medical time series are central to healthcare, enabling continuous monitoring and supporting timely clinical decisions. Despite recent progress, existing methods struggle to jointly model local-global dynamics and handle…
Time Series ClassificationGraph LearningTGFormer: Towards Temporal Graph Transformer with Auto-Correlation Mechanism
The growing interest in Temporal Graph Neural Networks (TGNNs) stems from their ability to model complex dynamics and deliver superior performance. However, TGNNs encounter fundamental challenges in capturing long-term d…
Time Series AnalysisGraph Learning