Papers Graph Learning
“Graph Learning” 태그가 달린 논문 2,014편 · 필터 해제
Each Graph is a New Language: Graph Learning with LLMs
Recent efforts leverage Large Language Models (LLMs) for modeling text-attributed graph structures in node classification tasks. These approaches describe graph structures for LLMs to understand or aggregate LLM-generate…
AttributeGraph LearningNode ClassificationTopology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things
With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribu…
AttributeGraph LearningSpatio-temporal Graph Learning on Adaptive Mined Key Frames for High-performance Multi-Object Tracking
In the realm of multi-object tracking, the challenge of accurately capturing the spatial and temporal relationships between objects in video sequences remains a significant hurdle. This is further complicated by frequent…
Graph LearningMulti-Object TrackingObject TrackingAdaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier edges. However, for temporal changes in ed…
Graph LearningLink PredictionNode ClassificationA Simple Graph Contrastive Learning Framework for Short Text Classification
Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown pro…
Contrastive LearningData AugmentationGraph Learningtext-classification+1Boosting Short Text Classification with Multi-Source Information Exploration and Dual-Level Contrastive Learning
Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named M…
Contrastive LearningGraph Learningtext-classificationText ClassificationEnhancing Graph Representation Learning with Localized Topological Features
Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. …
Graph LearningGraph Representation LearningLink PredictionNode Classification+1Predict Confidently, Predict Right: Abstention in Dynamic Graph Learning
Many real-world systems can be modeled as dynamic graphs, where nodes and edges evolve over time, requiring specialized models to capture their evolving dynamics in risk-sensitive applications effectively. Temporal graph…
Abstention PredictionDynamic Link PredictionDynamic Node ClassificationGraph Learning+3Active Sampling for Node Attribute Completion on Graphs
Node attribute, a type of crucial information for graph analysis, may be partially or completely missing for certain nodes in real world applications. Restoring the missing attributes is expected to benefit downstream gr…
AttributeGraph LearningGraph Contrastive Learning on Multi-label Classification for Recommendations
In business analysis, providing effective recommendations is essential for enhancing company profits. The utilization of graph-based structures, such as bipartite graphs, has gained popularity for their ability to analyz…
Contrastive LearningGraph LearningLink PredictionMulti-Label Classification+2A Heterogeneous Multimodal Graph Learning Framework for Recognizing User Emotions in Social Networks
The rapid expansion of social media platforms has provided unprecedented access to massive amounts of multimodal user-generated content. Comprehending user emotions can provide valuable insights for improving communicati…
Deep LearningEmotion RecognitionGraph LearningGraph Neural NetworkStructure-Preference Enabled Graph Embedding Generation under Differential Privacy
Graph embedding generation techniques aim to learn low-dimensional vectors for each node in a graph and have recently gained increasing research attention. Publishing low-dimensional node vectors enables various graph an…
Graph EmbeddingGraph LearningLink PredictionGraph-Based Multimodal and Multi-view Alignment for Keystep Recognition
Egocentric videos capture scenes from a wearer's viewpoint, resulting in dynamic backgrounds, frequent motion, and occlusions, posing challenges to accurate keystep recognition. We propose a flexible graph-learning frame…
Graph LearningNode ClassificationAdaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image
Hyperspectral image (HSI) clustering has been a fundamental but challenging task with zero training labels. Currently, some deep graph clustering methods have been successfully explored for HSI due to their outstanding p…
ClusteringGraph ClusteringGraph EmbeddingGraph Learning+1Long-range Brain Graph Transformer
Understanding communication and information processing among brain regions of interest (ROIs) is highly dependent on long-range connectivity, which plays a crucial role in facilitating diverse functional neural integrati…
Graph LearningGraph2text or Graph2token: A Perspective of Large Language Models for Graph Learning
Graphs are data structures used to represent irregular networks and are prevalent in numerous real-world applications. Previous methods directly model graph structures and achieve significant success. However, these meth…
Graph LearningAttriReBoost: A Gradient-Free Propagation Optimization Method for Cold Start Mitigation in Attribute Missing Graphs
Missing attribute issues are prevalent in the graph learning, leading to biased outcomes in Graph Neural Networks (GNNs). Existing methods that rely on feature propagation are prone to cold start problem, particularly wh…
AttributeComputational EfficiencyGPUGraph LearningMulti-modal Topology-embedded Graph Learning for Spatially Resolved Genes Prediction from Pathology Images with Prior Gene Similarity Information
The rapid development of spatial transcriptomics (ST) allows researchers to measure the spatial-level gene expression in tissues. Although powerful, the cost for collecting the ST data is expensive, and thus several …
Graph LearningGraph Neural NetworkPredictionGraphI2P: Image-to-Point Cloud Registration with Exploring Pattern of Correspondence via Graph Learning
Although the fusion of images and LiDAR point clouds is crucial to many applications in computer vision, the relative poses of cameras and LiDAR scanners are often unknown. The general registration pipeline first est…
Graph LearningGraph Neural NetworkImage to Point Cloud RegistrationPoint Cloud Registration+1FedSPA: Generalizable Federated Graph Learning under Homophily Heterogeneity
Federated Graph Learning (FGL) has emerged as a solution to address real-world privacy concerns and data silos in graph learning, which relies on Graph Neural Networks (GNNs). Nevertheless, the homophily level discre…
Graph Learning