Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs
The emergent reasoning capabilities of Large Language Models (LLMs) offer a transformative paradigm for analyzing text-attributed graphs. While instruction tuning is the prevailing method for adapting pre-trained LLMs to…
Node ClassificationGraph LearningOFA-MAS: One-for-All Multi-Agent System Topology Design based on Mixture-of-Experts Graph Generative Models
Multi-Agent Systems (MAS) offer a powerful paradigm for solving complex problems, yet their performance is critically dependent on the design of their underlying collaboration topology. As MAS become increasingly deploye…
Graph LearningARMARecon: An ARMA Convolutional Filter based Graph Neural Network for Neurodegenerative Dementias Classification
Early detection of neurodegenerative diseases such as Alzheimer's Disease (AD) and Frontotemporal Dementia (FTD) is essential for reducing the risk of progression to severe disease stages. As AD and FTD propagate along w…
Graph Neural NetworkGraph LearningCombating Spurious Correlations in Graph Interpretability via Self-Reflection
Interpretable graph learning has recently emerged as a popular research topic in machine learning. The goal is to identify the important nodes and edges of an input graph that are crucial for performing a specific graph …
Graph Representation LearningGraph LearningContext-aware Graph Causality Inference for Few-Shot Molecular Property Prediction
Molecular property prediction is becoming one of the major applications of graph learning in Web-based services, e.g., online protein structure prediction and drug discovery. A key challenge arises in few-shot scenarios,…
Molecular Property PredictionProtein Structure PredictionCausal InferenceGraph LearningSimple Network Graph Comparative Learning
The effectiveness of contrastive learning methods has been widely recognized in the field of graph learning, especially in contexts where graph data often lack labels or are difficult to label. However, the application o…
Contrastive LearningNode ClassificationGraph LearningFairGU: Fairness-aware Graph Unlearning in Social Networks
Graph unlearning has emerged as a critical mechanism for supporting sustainable and privacy-preserving social networks, enabling models to remove the influence of deleted nodes and thereby better safeguard user informati…
Graph LearningGraphSearch: Agentic Search-Augmented Reasoning for Zero-Shot Graph Learning
Recent advances in search-augmented large reasoning models (LRMs) enable the retrieval of external knowledge to reduce hallucinations in multistep reasoning. However, their ability to operate on graph-structured data, pr…
Node ClassificationLink PredictionGraph LearningKernel Alignment-based Multi-view Unsupervised Feature Selection with Sample-level Adaptive Graph Learning
Although multi-view unsupervised feature selection (MUFS) has demonstrated success in dimensionality reduction for unlabeled multi-view data, most existing methods reduce feature redundancy by focusing on linear correlat…
Dimensionality ReductionGraph LearningScalable Heterogeneous Graph Learning via Heterogeneous-aware Orthogonal Prototype Experts
Heterogeneous Graph Neural Networks(HGNNs) have advanced mainly through better encoders, yet their decoding/projection stage still relies on a single shared linear head, assuming it can map rich node embeddings to labels…
Graph LearningBridging Distance and Spectral Positional Encodings via Anchor-Based Diffusion Geometry Approximation
Molecular graph learning benefits from positional signals that capture both local neighborhoods and global topology. Two widely used families are spectral encodings derived from Laplacian or diffusion operators and ancho…
Graph LearningDDNet: A Dual-Stream Graph Learning and Disentanglement Framework for Temporal Forgery Localization
The rapid evolution of AIGC technology enables misleading viewers by tampering mere small segments within a video, rendering video-level detection inaccurate and unpersuasive. Consequently, temporal forgery localization …
Graph LearningScale-Adaptive Power Flow Analysis with Local Topology Slicing and Multi-Task Graph Learning
Developing deep learning models with strong adaptability to topological variations is of great practical significance for power flow analysis. To enhance model performance under variable system scales and improve robustn…
Graph LearningHeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs
Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for…
Heterogeneous Node ClassificationGraph LearningSpiking Heterogeneous Graph Attention Networks
Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing he…
Node ClassificationGraph LearningSpectral and Spatial Graph Learning for Multispectral Solar Image Compression
High-fidelity compression of multispectral solar imagery remains challenging for space missions, where limited bandwidth must be balanced against preserving fine spectral and spatial details. We present a learned image c…
Image CompressionGraph EmbeddingGraph LearningAnchorGK: Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging
Spatio-temporal kriging is a fundamental problem in sensor networks, driven by the sparsity of deployed sensors and the resulting missing observations. Although recent approaches model spatial and temporal correlations, …
Graph LearningSemantic Refinement with LLMs for Graph Representations
Graph-structured data exhibit substantial heterogeneity in where their predictive signals originate: in some domains, node-level semantics dominate, while in others, structural patterns play a central role. This structur…
Graph Representation LearningSemantic SimilarityGraph LearningSparScene: Efficient Traffic Scene Representation via Sparse Graph Learning for Large-Scale Trajectory Generation
Multi-agent trajectory generation is a core problem for autonomous driving and intelligent transportation systems. However, efficiently modeling the dynamic interactions between numerous road users and infrastructures in…
Autonomous DrivingGraph LearningCausal Heterogeneous Graph Learning Method for Chronic Obstructive Pulmonary Disease Prediction
Due to the insufficient diagnosis and treatment capabilities at the grassroots level, there are still deficiencies in the early identification and early warning of acute exacerbation of Chronic obstructive pulmonary dise…
Graph Representation LearningCausal InferenceGraph Learning