Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Advancing Graph Few-Shot Learning via In-Context Learning
Graph few-shot learning, which aims to classify nodes from novel classes with only a few labeled examples, is a widely studied problem in graph learning. However, existing methods often face two key limitations. First, t…
Few-Shot LearningGraph LearningBeyond the Aggregation Dilemma: Prior-Retaining Decoupled Learning for Multimodal Graphs
Multimodal Attributed Graph Learning (MAGL) integrates intrinsic node attributes with structural topology via graph aggregation. However, as pretrained encoders evolve into Large Foundation Models (LFMs), the landscape o…
Graph LearningS$^3$GNN: Efficient Global Mixing and Local Message Passing for Long-Range Graph Learning
Message-passing neural networks (MPNNs) often suffer from an information bottleneck when capturing long-range dependencies, leading to the oversquashing (OSQ) phenomenon. Alongside spatial connectivity enrichment (e.g., …
Graph LearningConcomitant DAG Learning: On the Roles of Noise Adaptivity, Sparsity, and Non-negativity
Directed acyclic graphs (DAGs) constitute a central modeling tool to enable principled reasoning about cause-effect interactions in complex systems. However, since the causal structure underlying a group of variables is …
Causal InferenceGraph LearningA2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction
Dynamic link prediction is important for modeling evolving interactions in complex systems, including social, communication, financial, and transportation networks. Classical temporal graph models capture sequential depe…
Dynamic Link PredictionGraph LearningLost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
Transformers have become a central architecture for graph learning, but their application to graphs requires first choosing a tokenization: a graph-to-token map that determines which structural information is exposed at …
Graph LearningA Unified Framework for Structure-Aware Clustering and Heterogeneous Causal Graph Learning
In complex multivariate systems, interactions among variables are defined by dependency structures, often encoded as directed acyclic graphs ($\text{DAGs}$). However, dependency structures can vary across subjects, and i…
Graph LearningWaveGraphNet: Physics-Consistent Guided-Wave Damage Localization through Coupled Inverse-Forward Graph Learning
Guided-wave structural health monitoring enables damage localization in composite plates using sparse networks of bonded piezoelectric transducers. However, inferring the spatial location of defects from pitch-catch meas…
Graph LearningExpressive Power of Deep Homomorphism Networks over Relational Databases
The expressive limitations of message-passing Graph Neural Networks (GNNs) have motivated a wide range of more powerful graph learning architectures. We advocate Deep Homomorphism Networks (DHNs) as a model particularly …
Graph LearningGraph Hierarchical Recurrence for Long-Range Generalization
Graph Neural Networks and Graph Transformers have become central to graph learning, combining expressive representation learning with sample-efficient inductive biases. Yet they remain fundamentally limited when predicti…
Graph LearningGraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction
Computed tomography (CT) metal artifact reduction (MAR) aims to reduce the severe streaking artifacts induced by metallic implants and other high-density objects. Effective MAR generally requires both accurate artifact l…
Graph Learning1GC-7RC: One Graphic Card -- Seven Research Challenges! How Good Are AI Agents at Doing Your Job?
Autonomous AI coding agents are becoming a core tool for ML practitioners in industry and research alike. Despite this growing adoption, no standardized benchmark exists to evaluate their ability to design, implement, an…
Semantic SegmentationImage ClassificationText ClassificationGraph LearningGaussian Relational Graph Transformer
Relational graph learning models relational databases as graphs and has demonstrated superior performance on a wide range of relational predictive tasks. However, existing methods struggle to capture long-range dependenc…
Graph LearningGRID: Graph Representation of Intelligence Data for Security Text Knowledge Graph Construction
Security knowledge graphs can provide computable external memory for security agents, but constructing them from long-form cyber threat intelligence (CTI) remains difficult: LLMs often lack grounded security-domain knowl…
Knowledge GraphsGraph LearningGFMate: Empowering Graph Foundation Models with Test-time Prompt Tuning
Graph prompt tuning has shown great potential in graph learning by introducing trainable prompts to enhance the model performance in conventional single-domain scenarios. Recent research has extended graph prompts to imp…
Graph LearningDRIFT: A Benchmark for Task-Free Continual Graph Learning with Continuous Distribution Shifts
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formulation, where the data stream is partition…
Continual LearningGraph LearningDecoupled and Divergence-Conditioned Prompt for Multi-domain Dynamic Graph Foundation Models
Dynamic graphs are ubiquitous in real-world systems, and building generalizable dynamic Graph Foundation Models has become a frontier in graph learning. However, dynamic graphs from different domains pose fundamental cha…
Node ClassificationDomain AdaptationLink PredictionGraph LearningCAMPA: Efficient and Aligned Multimodal Graph Learning via Decoupled Propagation and Aggregation
Multimodal Graph Neural Networks (MGNNs) have shown strong potential for learning from multimodal attributed graphs, yet most existing approaches rely on tightly coupled architectures that suffer from prohibitive computa…
Representation LearningGraph LearningLearning Subspace-Preserving Sparse Attention Graphs from Heterogeneous Multiview Data
The high-dimensional features extracted from large-scale unlabeled data via various pretrained models with diverse architectures are referred to as heterogeneous multiview data. Most existing unsupervised transfer learni…
Representation LearningTransfer LearningGraph LearningSTAGE: Tackling Semantic Drift in Multimodal Federated Graph Learning
Federated graph learning (FGL) enables collaborative training on graph data across multiple clients. As graph data increasingly contain multimodal node attributes such as text and images, multimodal federated graph learn…
Graph Learning