Papers Graph Representation Learning
“Graph Representation Learning” 태그가 달린 논문 1,123편 · 필터 해제
SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning
Textual graphs (TGs) are graphs whose nodes correspond to text (sentences or documents), which are widely prevalent. The representation learning of TGs involves two stages: (i) unsupervised feature extraction and (ii) su…
Feature EngineeringGraph LearningGraph Representation LearningLink Prediction+3Graph Contrastive Learning with Generative Adversarial Network
Graph Neural Networks (GNNs) have demonstrated promising results on exploiting node representations for many downstream tasks through supervised end-to-end training. To deal with the widespread label scarcity issue in re…
Contrastive LearningData AugmentationGenerative Adversarial NetworkGraph Representation Learning+2Gradient-Based Spectral Embeddings of Random Dot Product Graphs
The Random Dot Product Graph (RDPG) is a generative model for relational data, where nodes are represented via latent vectors in low-dimensional Euclidean space. RDPGs crucially postulate that edge formation probabilitie…
Graph Representation LearningNetwork EmbeddingRepresentation LearningEchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiograms
The functional assessment of the left ventricle chamber of the heart requires detecting four landmark locations and measuring the internal dimension of the left ventricle and the approximate mass of the surrounding muscl…
Graph Neural NetworkGraph Representation LearningRepresentation LearningExpert Knowledge-Aware Image Difference Graph Representation Learning for Difference-Aware Medical Visual Question Answering
To contribute to automating the medical vision-language model, we propose a novel Chest-Xray Difference Visual Question Answering (VQA) task. Given a pair of main and reference images, this task attempts to answer severa…
Graph Representation LearningLanguage ModelingLanguage ModellingMedical Visual Question Answering+4From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs
Many real-world datasets have an underlying dynamic graph structure, where entities and their interactions evolve over time. Machine learning models should consider these dynamics in order to harness their full potential…
Graph Representation LearningNode ClassificationRepresentation LearningNeural Architecture Retrieval
With the increasing number of new neural architecture designs and substantial existing neural architectures, it becomes difficult for the researchers to situate their contributions compared with existing neural architect…
Contrastive LearningGraph Representation LearningRepresentation LearningRetrievalDistTGL: Distributed Memory-Based Temporal Graph Neural Network Training
Memory-based Temporal Graph Neural Networks are powerful tools in dynamic graph representation learning and have demonstrated superior performance in many real-world applications. However, their node memory favors smalle…
GPUGraph Neural NetworkGraph Representation LearningRepresentation LearningFrameless Graph Knowledge Distillation
Knowledge distillation (KD) has shown great potential for transferring knowledge from a complex teacher model to a simple student model in which the heavy learning task can be accomplished efficiently and without losing …
Graph Representation LearningKnowledge DistillationRepresentation LearningImproving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold
In graph representation learning, it is important that the complex geometric structure of the input graph, e.g. hidden relations among nodes, is well captured in embedding space. However, standard Euclidean embedding spa…
Graph EmbeddingGraph LearningGraph Representation LearningKnowledge Graph Embedding+2Neural Causal Graph Collaborative Filtering
Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn u…
Collaborative FilteringGraph LearningGraph Representation LearningRecommendation Systems+2Histopathology Whole Slide Image Analysis with Heterogeneous Graph Representation Learning
Graph-based methods have been extensively applied to whole-slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different entities. However, most of the existing meth…
AttributeGraph Representation LearningPseudo LabelRepresentation Learning+2ENGAGE: Explanation Guided Data Augmentation for Graph Representation Learning
The recent contrastive learning methods, due to their effectiveness in representation learning, have been widely applied to modeling graph data. Random perturbation is widely used to build contrastive views for graph dat…
Contrastive LearningData AugmentationGraph Representation LearningRepresentation LearningGraph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective
Graph neural networks (GNNs) have pioneered advancements in graph representation learning, exhibiting superior feature learning and performance over multilayer perceptrons (MLPs) when handling graph inputs. However, unde…
Graph Representation LearningLearning TheoryMemorizationRepresentation LearningOtter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences.…
Drug DiscoveryGraph Representation LearningKnowledge GraphsPrediction+1Directional diffusion models for graph representation learning
In recent years, diffusion models have achieved remarkable success in various domains of artificial intelligence, such as image synthesis, super-resolution, and 3D molecule generation. However, the application of diffusi…
3D Molecule GenerationDenoisingGraph LearningGraph Representation Learning+3Transforming Graphs for Enhanced Attribute Clustering: An Innovative Graph Transformer-Based Method
Graph Representation Learning (GRL) is an influential methodology, enabling a more profound understanding of graph-structured data and aiding graph clustering, a critical task across various domains. The recent incursion…
AttributeClusteringGraph AttentionGraph Clustering+4Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has contributed significantly to breakthroughs in various fields, including biomedicine. The objective of this survey is to review the latest advanc…
Graph Representation LearningRepresentation LearningSurveyMixed-Curvature Transformers for Graph Representation Learning papersreview
Real-world graphs naturally exhibit hierarchical or cyclical structures that are unfit for the typical Euclidean space. While there exist graph neural networks that leverage non-Euclidean spaces to embed such structures …
Graph ReconstructionGraph Representation LearningNode ClassificationRepresentation LearningAccelerating Dynamic Network Embedding with Billions of Parameter Updates to Milliseconds
Network embedding, a graph representation learning method illustrating network topology by mapping nodes into lower-dimension vectors, is challenging to accommodate the ever-changing dynamic graphs in practice. Existing …
Graph ReconstructionGraph Representation LearningLink PredictionNetwork Embedding+2