Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
GREAT-EER: Graph Edge Attention Network for Emergency Evacuation Responses
Emergency situations that require the evacuation of urban areas can arise from man-made causes (e.g., terrorist attacks or industrial accidents) or natural disasters, the latter becoming more frequent due to climate chan…
Reinforcement LearningGraph LearningNeuroMambaLLM: Dynamic Graph Learning of fMRI Functional Connectivity in Autistic Brains Using Mamba and Language Model Reasoning
Large Language Models (LLMs) have demonstrated strong semantic reasoning across multimodal domains. However, their integration with graph-based models of brain connectivity remains limited. In addition, most existing fMR…
Graph LearningSpaTeoGL: Spatiotemporal Graph Learning for Interpretable Seizure Onset Zone Analysis from Intracranial EEG
Accurate localization of the seizure onset zone (SOZ) from intracranial EEG (iEEG) is essential for epilepsy surgery but is challenged by complex spatiotemporal seizure dynamics. We propose SpaTeoGL, a spatiotemporal gra…
Graph LearningStructural Bias Beyond Homophily: A Study of Fairness in Link Prediction
Graph link prediction (LP) plays a critical role in socially impactful applications such as job recommendation and friendship formation, making fairness a critical concern in this task. While many fairness-aware methods …
Graph GenerationLink PredictionGraph LearningCommunity Concealment from Unsupervised Graph Learning-Based Clustering
Graph neural networks (GNNs) are designed to use attributed graphs to learn representations. Such representations are beneficial in the unsupervised learning of clusters and community detection. Nonetheless, such inferen…
Community DetectionGraph LearningDetecting Brick Kiln Infrastructure at Scale: Graph, Foundation, and Remote Sensing Models for Satellite Imagery Data
Brick kilns are a major source of air pollution and forced labor in South Asia, yet large-scale monitoring remains limited by sparse and outdated ground data. We study brick kiln detection at scale using high-resolution …
Graph LearningRiemannGL: Riemannian Geometry Changes Graph Deep Learning
Graphs are ubiquitous, and learning on graphs has become a cornerstone in artificial intelligence and data mining communities. Unlike pixel grids in images or sequential structures in language, graphs exhibit a typical n…
Graph Representation LearningGraph LearningDifferentiable Tripartite Modularity for Clustering Heterogeneous Graphs
Clustering heterogeneous relational data remains a central challenge in graph learning, particularly when interactions involve more than two types of entities. While differentiable modularity objectives such as DMoN have…
Graph Neural NetworkCommunity DetectionGraph LearningGraph Learning Should Move Beyond Restrictive Views of Spectral and Message-Passing GNNs
Graph neural networks (GNNs) are commonly divided into message-passing neural networks (MPNNs) and spectral GNNs, reflecting two largely separate research traditions in machine learning and signal processing. While MPNNs…
Graph LearningEnhanced Graph Transformer with Serialized Graph Tokens
Transformers have demonstrated success in graph learning, particularly for node-level tasks. However, existing methods encounter an information bottleneck when generating graph-level representations. The prevalent single…
Graph LearningLLM-Enhanced Rumor Detection via Virtual Node Induced Edge Prediction
The rapid proliferation of rumors on social networks poses a significant threat to information integrity. While rumor dissemination forms complex structural patterns, existing detection methods often fail to capture the …
Graph LearningGraph homophily booster: Reimagining the role of discrete features in heterophilic graph learning
Graph neural networks (GNNs) have emerged as a powerful tool for modeling graph-structured data. However, existing GNNs often struggle with heterophilic graphs, where connected nodes tend to have dissimilar features or l…
Graph LearningCFRecs: Counterfactual Recommendations on Real Estate User Listing Interaction Graphs
Graph-structured data is ubiquitous and powerful in representing complex relationships in many online platforms. While graph neural networks (GNNs) are widely used to learn from such data, counterfactual graph learning h…
Graph Neural NetworkGraph LearningBillion-Scale Graph Foundation Models
Graph-structured data underpins many critical applications. While foundation models have transformed language and vision via large-scale pretraining and lightweight adaptation, extending this paradigm to general, real-wo…
Graph LearningCausal Graph Learning via Distributional Invariance of Cause-Effect Relationship
This paper introduces a new framework for recovering causal graphs from observational data, leveraging the observation that the distribution of an effect, conditioned on its causes, remains invariant to changes in the pr…
Graph LearningPIMPC-GNN: Physics-Informed Multi-Phase Consensus Learning for Enhancing Imbalanced Node Classification in Graph Neural Networks
Graph neural networks (GNNs) often struggle in class-imbalanced settings, where minority classes are under-represented and predictions are biased toward majorities. We propose \textbf{PIMPC-GNN}, a physics-informed multi…
Node ClassificationGraph LearningA Causal Perspective for Enhancing Jailbreak Attack and Defense
Uncovering the mechanisms behind "jailbreaks" in large language models (LLMs) is crucial for enhancing their safety and reliability, yet these mechanisms remain poorly understood. Existing studies predominantly analyze j…
Graph LearningTemporal Graph Pattern Machine
Temporal graph learning is pivotal for deciphering dynamic systems, where the core challenge lies in explicitly modeling the underlying evolving patterns that govern network transformation. However, prevailing methods ar…
Node ClassificationLink PredictionGraph LearningHeterogeneous Graph Alignment for Joint Reasoning and Interpretability
Multi-graph learning is crucial for extracting meaningful signals from collections of heterogeneous graphs. However, effectively integrating information across graphs with differing topologies, scales, and semantics, oft…
Graph LearningA Cross-Domain Graph Learning Protocol for Single-Step Molecular Geometry Refinement
Accurate molecular geometries are a prerequisite for reliable quantum-chemical predictions, yet density functional theory (DFT) optimization remains a major bottleneck for high-throughput molecular screening. Here we pre…
Graph Learning