Papers Graph Embedding
“Graph Embedding” 태그가 달린 논문 1,236편 · 필터 해제
Graph Embedding in the Graph Fractional Fourier Transform Domain
Spectral graph embedding plays a critical role in graph representation learning by generating low-dimensional vector representations from graph spectral information. However, the embedding space of traditional spectral e…
Graph Representation LearningGraph EmbeddingOKG-LLM: Aligning Ocean Knowledge Graph with Observation Data via LLMs for Global Sea Surface Temperature Prediction
Sea surface temperature (SST) prediction is a critical task in ocean science, supporting various applications, such as weather forecasting, fisheries management, and storm tracking. While existing data-driven methods hav…
Weather ForecastingGraph EmbeddingGUARD-CAN: Graph-Understanding and Recurrent Architecture for CAN Anomaly Detection
Modern in-vehicle networks face various cyber threats due to the lack of encryption and authentication in the Controller Area Network (CAN). To address this security issue, this paper presents GUARD-CAN, an anomaly detec…
Representation LearningFeature EngineeringAnomaly DetectionGraph EmbeddingSMART: Relation-Aware Learning of Geometric Representations for Knowledge Graphs
Knowledge graph representation learning approaches provide a mapping between symbolic knowledge in the form of triples in a knowledge graph (KG) and their feature vectors. Knowledge graph embedding (KGE) models often rep…
Graph EmbeddingGraph Representation LearningKnowledge Graph EmbeddingKnowledge Graphs+1Soft Graph Clustering for single-cell RNA Sequencing Data
Clustering analysis is fundamental in single-cell RNA sequencing (scRNA-seq) data analysis for elucidating cellular heterogeneity and diversity. Recent graph-based scRNA-seq clustering methods, particularly graph neural …
Computational EfficiencyGraph ClusteringGraph EmbeddingMetapath-based Hyperbolic Contrastive Learning for Heterogeneous Graph Embedding
The hyperbolic space, characterized by a constant negative curvature and exponentially expanding space, aligns well with the structural properties of heterogeneous graphs. However, although heterogeneous graphs inherentl…
Contrastive LearningGraph EmbeddingETT-CKGE: Efficient Task-driven Tokens for Continual Knowledge Graph Embedding
Continual Knowledge Graph Embedding (CKGE) seeks to integrate new knowledge while preserving past information. However, existing methods struggle with efficiency and scalability due to two key limitations: (1) suboptimal…
Graph EmbeddingKnowledge Graph EmbeddingTransfer LearningEfficient Identity and Position Graph Embedding via Spectral-Based Random Feature Aggregation
Graph neural networks (GNNs), which capture graph structures via a feature aggregation mechanism following the graph embedding framework, have demonstrated a powerful ability to support various tasks. According to the to…
Graph EmbeddingPositionPredicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction to KGE methods, providing uncertainty e…
Conformal PredictionGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graph Embeddings+2Lightweight Spatio-Temporal Attention Network with Graph Embedding and Rotational Position Encoding for Traffic Forecasting
Traffic forecasting is a key task in the field of Intelligent Transportation Systems. Recent research on traffic forecasting has mainly focused on combining graph neural networks (GNNs) with other models. However, GNNs o…
Feature EngineeringGraph EmbeddingPositionRobust Knowledge Graph Embedding via Denoising
We focus on obtaining robust knowledge graph embedding under perturbation in the embedding space. To address these challenges, we introduce a novel framework, Robust Knowledge Graph Embedding via Denoising, which enhance…
DenoisingGraph EmbeddingKnowledge Graph EmbeddingInjecting Knowledge Graphs into Large Language Models
Integrating structured knowledge from Knowledge Graphs (KGs) into Large Language Models (LLMs) remains a key challenge for symbolic reasoning. Existing methods mainly rely on prompt engineering or fine-tuning, which lose…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsPrompt EngineeringElectricity Cost Minimization for Multi-Workflow Allocation in Geo-Distributed Data Centers
Worldwide, Geo-distributed Data Centers (GDCs) provide computing and storage services for massive workflow applications, resulting in high electricity costs that vary depending on geographical locations and time. How to …
Graph EmbeddingSchedulingQuatE-D: A Distance-Based Quaternion Model for Knowledge Graph Embedding
Knowledge graph embedding (KGE) methods aim to represent entities and relations in a continuous space while preserving their structural and semantic properties. Quaternion-based KGEs have demonstrated strong potential in…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingBalancing Graph Embedding Smoothness in Self-Supervised Learning via Information-Theoretic Decomposition
Self-supervised learning (SSL) in graphs has garnered significant attention, particularly in employing Graph Neural Networks (GNNs) with pretext tasks initially designed for other domains, such as contrastive learning an…
Contrastive LearningGraph EmbeddingLink PredictionNode Classification+1On Large-scale Evaluation of Embedding Models for Knowledge Graph Completion
Knowledge graph embedding (KGE) models are extensively studied for knowledge graph completion, yet their evaluation remains constrained by unrealistic benchmarks. Standard evaluation metrics rely on the closed-world assu…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Optimal Embedding Guided Negative Sample Generation for Knowledge Graph Link Prediction
Knowledge graph embedding (KGE) models encode the structural information of knowledge graphs to predicting new links. Effective training of these models requires distinguishing between positive and negative samples with …
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsLink PredictionSCMPPI: Supervised Contrastive Multimodal Framework for Predicting Protein-Protein Interactions
Protein-protein interaction (PPI) prediction plays a pivotal role in deciphering cellular functions and disease mechanisms. To address the limitations of traditional experimental methods and existing computational approa…
Contrastive LearningGraph EmbeddingPredictionEmbedding Method for Knowledge Graph with Densely Defined Ontology
Knowledge graph embedding (KGE) is a technique that enhances knowledge graphs by addressing incompleteness and improving knowledge retrieval. A limitation of the existing KGE models is their underutilization of ontologie…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsRetrievalAdvSGM: Differentially Private Graph Learning via Adversarial Skip-gram Model
The skip-gram model (SGM), which employs a neural network to generate node vectors, serves as the basis for numerous popular graph embedding techniques. However, since the training datasets contain sensitive linkage info…
Graph EmbeddingGraph Learning