Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Sequence Diffusion Model for Temporal Link Prediction in Continuous-Time Dynamic Graph
Temporal link prediction in dynamic graphs is a fundamental problem in many real-world systems. Existing temporal graph neural networks mainly focus on learning representations of historical interactions. Despite their s…
Link PredictionGraph LearningLION: A Clifford Neural Paradigm for Multimodal-Attributed Graph Learning
Recently, the rapid advancement of multimodal domains has driven a data-centric paradigm shift in graph ML, transitioning from text-attributed to multimodal-attributed graphs. This advancement significantly enhances data…
Graph LearningMM-OpenFGL: A Comprehensive Benchmark for Multimodal Federated Graph Learning
Multimodal-attributed graphs (MMAGs) provide a unified framework for modeling complex relational data by integrating heterogeneous modalities with graph structures. While centralized learning has shown promising performa…
Graph LearningCCMamba: Topologically-Informed Selective State-Space Networks on Combinatorial Complexes for Higher-Order Graph Learning
Topological deep learning has emerged as a powerful paradigm for modeling higher-order relational structures beyond pairwise interactions that standard graph neural networks fail to capture. While combinatorial complexes…
Graph LearningExact Graph Learning via Integer Programming
Learning the dependence structure among variables in complex systems is a central problem across medical, natural, and social sciences. These structures can be naturally represented by graphs, and the task of inferring s…
Graph LearningOut-of-Distribution Generalization in Graph Foundation Models
Graphs are a fundamental data structure for representing relational information in domains such as social networks, molecular systems, and knowledge graphs. However, graph learning models often suffer from limited genera…
Knowledge GraphsGraph LearningGraphSB: Boosting Imbalanced Node Classification on Graphs through Structural Balance
Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized…
Node ClassificationGraph LearningFixed Aggregation Features Can Rival GNNs
Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations. We challenge this view by introducing Fixed Aggregation Features (FAFs), a training-f…
Representation LearningGraph LearningGraphDLG: Exploring Deep Leakage from Gradients in Federated Graph Learning
Federated graph learning (FGL) has recently emerged as a promising privacy-preserving paradigm that enables distributed graph learning across multiple data owners. A critical privacy concern in federated learning is whet…
Federated LearningGraph LearningGCFX: Generative Counterfactual Explanations for Deep Graph Models at the Model Level
Deep graph learning models have demonstrated remarkable capabilities in processing graph-structured data and have been widely applied across various fields. However, their complex internal architectures and lack of trans…
Graph GenerationGraph LearningGraIP: A Benchmarking Framework For Neural Graph Inverse Problems
A wide range of graph learning tasks, such as structure discovery, temporal graph analysis, and combinatorial optimization, focus on inferring graph structures from data, rather than making predictions on given graphs. H…
Graph LearningFedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
Virtual Asset Service Providers (VASPs) face a fundamental tension between regulatory compliance and user privacy when detecting cross-institutional money laundering. Current approaches require either sharing sensitive t…
Graph Neural NetworkFraud DetectionGraph LearningDREAM: Dual-Standard Semantic Homogeneity with Dynamic Optimization for Graph Learning with Label Noise
Graph neural networks (GNNs) have been widely used in various graph machine learning scenarios. Existing literature primarily assumes well-annotated training graphs, while the reliability of labels is not guaranteed in r…
Graph LearningBoostFGL: Boosting Fairness in Federated Graph Learning
Federated graph learning (FGL) enables collaborative training of graph neural networks (GNNs) across decentralized subgraphs without exposing raw data. While existing FGL methods often achieve high overall accuracy, we s…
Graph LearningDANCE: Dynamic, Available, Neighbor-gated Condensation for Federated Text-Attributed Graphs
Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. With the rise of large language models (LLMs), textual attributes in FGL graphs are gaining attention. Text-attributed …
Federated LearningGraph LearningFine-Grained Traceability for Transparent ML Pipelines
Modern machine learning systems are increasingly realised as multistage pipelines, yet existing transparency mechanisms typically operate at a model level: they describe what a system is and why it behaves as it does, bu…
Graph LearningRAG-GFM: Overcoming In-Memory Bottlenecks in Graph Foundation Models via Retrieval-Augmented Generation
Graph Foundation Models (GFMs) have emerged as a frontier in graph learning, which are expected to deliver transferable representations across diverse tasks. However, GFMs remain constrained by in-memory bottlenecks: the…
Graph ClassificationGraph LearningResilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning
With the rapid development of the e-commerce industry, the logistics network is experiencing unprecedented pressure. The traditional static routing strategy most time cannot tolerate the traffic congestion and fluctuatin…
Graph LearningModality as Heterogeneity: Node Splitting and Graph Rewiring for Multimodal Graph Learning
Multimodal graphs are gaining increasing attention due to their rich representational power and wide applicability, yet they introduce substantial challenges arising from severe modality confusion. To address this issue,…
Graph LearningTopology-Aware Multiscale Mixture of Experts for Efficient Molecular Property Prediction
Many molecular properties depend on 3D geometry, where non-covalent interactions, stereochemical effects, and medium- to long-range forces are determined by spatial distances and angles that cannot be uniquely captured b…
Molecular Property PredictionGraph Learning