Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Neural Message-Passing on Attention Graphs for Hallucination Detection
Large Language Models (LLMs) often generate incorrect or unsupported content, known as hallucinations. Existing detection methods rely on heuristics or simple models over isolated computational traces such as activations…
Graph LearningVirtual Nodes based Heterogeneous Graph Convolutional Neural Network for Efficient Long-Range Information Aggregation
Heterogeneous Graph Neural Networks (HGNNs) have exhibited powerful performance in heterogeneous graph learning by aggregating information from various types of nodes and edges. However, existing heterogeneous graph mode…
Graph LearningOn the Sheafification of Higher-Order Message Passing
Recent work in Topological Deep Learning (TDL) seeks to generalize graph learning's preeminent $message \ passing$ paradigm to more complex relational structures: simplicial complexes, cell complexes, hypergraphs, and co…
Graph LearningA Generative Model for Controllable Feature Heterophily in Graphs
We introduce a principled generative framework for graph signals that enables explicit control of feature heterophily, a key property underlying the effectiveness of graph learning methods. Our model combines a Lipschitz…
Graph LearningBrain PathoGraph Learning
Brain graph learning has demonstrated significant achievements in the fields of neuroscience and artificial intelligence. However, existing methods struggle to selectively learn disease-related knowledge, leading to heav…
Computational EfficiencyGraph LearningRotary Position Encodings for Graphs
We study the extent to which rotary position encodings (RoPE), a recent transformer position encoding algorithm broadly adopted in large language models (LLMs) and vision transformers (ViTs), can be applied to graph-stru…
Graph LearningGraphUniverse: Synthetic Graph Generation for Evaluating Inductive Generalization
A fundamental challenge in graph learning is understanding how models generalize to new, unseen graphs. While synthetic benchmarks offer controlled settings for analysis, existing approaches are confined to single-graph,…
Graph GenerationGraph LearningSpatio-Temporal Directed Graph Learning for Account Takeover Fraud Detection
Account Takeover (ATO) fraud poses a significant challenge in consumer banking, requiring high recall under strict latency while minimizing friction for legitimate users. Production systems typically rely on tabular grad…
Node ClassificationFraud DetectionGraph LearningFast, Accurate and Interpretable Graph Classification with Topological Kernels
We introduce a novel class of explicit feature maps based on topological indices that represent each graph by a compact feature vector, enabling fast and interpretable graph classification. Using radial basis function ke…
Graph ClassificationGraph LearningFrom domain-landmark graph learning to problem-landmark graph generation
Landmarks have long played a pivotal role in automated planning, serving as crucial elements for improving the planning algorithms. The main limitation of classical landmark extraction methods is their sensitivity to spe…
Graph GenerationGraph LearningSubteaming and Adaptive Formation Control for Coordinated Multi-Robot Navigation
Coordinated multi-robot navigation is essential for robots to operate as a team in diverse environments. During navigation, robot teams usually need to maintain specific formations, such as circular formations to protect…
Robot NavigationGraph LearningOne-step Multi-view Clustering With Adaptive Low-rank Anchor-graph Learning
In light of their capability to capture structural information while reducing computing complexity, anchor graph-based multi-view clustering (AGMC) methods have attracted considerable attention in large-scale clustering …
Graph LearningLearning Graph from Smooth Signals under Partial Observation: A Robustness Analysis
Learning the graph underlying a networked system from nodal signals is crucial to downstream tasks in graph signal processing and machine learning. The presence of hidden nodes whose signals are not observable might corr…
Graph LearningAttention Beyond Neighborhoods: Reviving Transformer for Graph Clustering
Attention mechanisms have become a cornerstone in modern neural networks, driving breakthroughs across diverse domains. However, their application to graph structured data, where capturing topological connections is esse…
Computational EfficiencyInformation ExtractionGraph ClusteringGraph LearningState Space Models over Directed Graphs
Directed graphs are ubiquitous across numerous domains, where the directionality of edges encodes critical causal dependencies. However, existing GNNs and graph Transformers tailored for directed graphs face two major ch…
Graph Neural NetworkGraph LearningDissecting Federated-Graph Aggregation under Domain Shift: Importance-Aware Aggregation via Empirical Analysis
Federated graph learning (FGL) trains a shared graph model across clients whose local graphs differ in node features, labels, and connectivity while keeping raw graph data decentralized. Although graph-domain shifts acro…
Graph LearningA Geometric Graph-Based Deep Learning Model for Drug-Target Affinity Prediction
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge. Recent advances in artificial intelligence, particularly deep learnin…
Graph LearningDrug DiscoveryHoldout Set3DViT-GAT: A Unified Atlas-Based 3D Vision Transformer and Graph Learning Framework for Major Depressive Disorder Detection Using Structural MRI Data
Major depressive disorder (MDD) is a prevalent mental health condition that negatively impacts both individual well-being and global public health. Automated detection of MDD using structural magnetic resonance imaging (…
Graph Neural NetworkGraph LearningQuantum Graph Attention Networks: Trainable Quantum Encoders for Inductive Graph Learning
We introduce Quantum Graph Attention Networks (QGATs) as trainable quantum encoders for inductive learning on graphs, extending the Quantum Graph Neural Networks (QGNN) framework. QGATs leverage parameterized quantum cir…
Graph LearningImproving Greenland Bed Topography Mapping with Uncertainty-Aware Graph Learning on Sparse Radar Data
Accurate maps of Greenland's subglacial bed are essential for sea-level projections, but radar observations are sparse and uneven. We introduce GraphTopoNet, a graph-learning framework that fuses heterogeneous supervisio…
Graph Learning