Papers Graph Embedding
“Graph Embedding” 태그가 달린 논문 1,236편 · 필터 해제
Multi-dataset and Transfer Learning Using Gene Expression Knowledge Graphs
Gene expression datasets offer insights into gene regulation mechanisms, biochemical pathways, and cellular functions. Additionally, comparing gene expression profiles between disease and control patients can deepen the …
Data IntegrationGraph EmbeddingGraph Neural NetworkKnowledge Graph Embedding+2DiffMove: Group Mobility Tendency Enhanced Trajectory Recovery via Diffusion Model
In the real world, trajectory data is often sparse and incomplete due to low collection frequencies or limited device coverage. Trajectory recovery aims to recover these missing trajectory points, making the trajectories…
Graph EmbeddingTrajectory RecoveryFinsler Multi-Dimensional Scaling: Manifold Learning for Asymmetric Dimensionality Reduction and Embedding
Dimensionality reduction is a fundamental task that aims to simplify complex data by reducing its feature dimensionality while preserving essential patterns, with core applications in data analysis and visualisation. To …
Dimensionality ReductionGraph EmbeddingLink PredictionHow Feasible is Augmenting Fake Nodes with Learnable Features as a Counter-strategy against Link Stealing Attacks?
Graph Neural Networks (GNNs) are widely used and deployed for graph-based prediction tasks. However, as good as GNNs are for learning graph data, they also come with the risk of privacy leakage. For instance, an attacker…
Graph EmbeddingSoTCKGE:Continual Knowledge Graph Embedding Based on Spatial Offset Transformation
Current Continual Knowledge Graph Embedding (CKGE) methods primarily rely on translation-based embedding methods, leveraging previously acquired knowledge to initialize new facts. To enhance learning efficiency, these me…
Continual LearningGraph EmbeddingKnowledge Graph EmbeddingTranslationA Zero-shot Learning Method Based on Large Language Models for Multi-modal Knowledge Graph Embedding
Zero-shot learning (ZL) is crucial for tasks involving unseen categories, such as natural language processing, image classification, and cross-lingual transfer. Current applications often fail to accurately infer and han…
Cross-Lingual TransferGraph Embeddingimage-classificationImage Classification+4Deep Cut-informed Graph Embedding and Clustering
Graph clustering aims to divide the graph into different clusters. The recently emerging deep graph clustering approaches are largely built on graph neural networks (GNN). However, GNN is designed for general graph encod…
AttributeClusteringGraph ClusteringGraph Embedding+1FHGE: A Fast Heterogeneous Graph Embedding with Ad-hoc Meta-paths
Graph neural networks (GNNs) have emerged as the state of the art for a variety of graph-related tasks and have been widely used in Heterogeneous Graphs (HetGs), where meta-paths help encode specific semantics between va…
Graph EmbeddingLink PredictionNode Classification$k$-Graph: A Graph Embedding for Interpretable Time Series Clustering
Time series clustering poses a significant challenge with diverse applications across domains. A prominent drawback of existing solutions lies in their limited interpretability, often confined to presenting users with ce…
ClusteringGraph EmbeddingTime SeriesTime Series ClusteringRecent Advances in Malware Detection: Graph Learning and Explainability
The rapid evolution of malware has necessitated the development of sophisticated detection methods that go beyond traditional signature-based approaches. Graph learning techniques have emerged as powerful tools for model…
Feature EngineeringGraph EmbeddingGraph LearningMalware Analysis+2Spectral Journey: How Transformers Predict the Shortest Path
Decoder-only transformers lead to a step-change in capability of large language models. However, opinions are mixed as to whether they are really planning or reasoning. A path to making progress in this direction is to s…
DecoderGraph EmbeddingLiDAR Loop Closure Detection using Semantic Graphs with Graph Attention Networks
In this paper, we propose a novel loop closure detection algorithm that uses graph attention neural networks to encode semantic graphs to perform place recognition and then use semantic registration to estimate the 6 DoF…
Graph AttentionGraph EmbeddingLoop Closure DetectionA Geometric Perspective for High-Dimensional Multiplex Graphs
High-dimensional multiplex graphs are characterized by their high number of complementary and divergent dimensions. The existence of multiple hierarchical latent relations between the graph dimensions poses significant c…
Graph EmbeddingTransformer-Based Multimodal Knowledge Graph Completion with Link-Aware Contexts
Multimodal knowledge graph completion (MMKGC) aims to predict missing links in multimodal knowledge graphs (MMKGs) by leveraging information from various modalities alongside structural data. Existing MMKGC approaches pr…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge GraphsIntegrate Temporal Graph Learning into LLM-based Temporal Knowledge Graph Model
Temporal Knowledge Graph Forecasting (TKGF) aims to predict future events based on the observed events in history. Recently, Large Language Models (LLMs) have exhibited remarkable capabilities, generating significant res…
cross-modal alignmentGraph EmbeddingGraph LearningKnowledge GraphsReducing the Sensitivity of Neural Physics Simulators to Mesh Topology via Pretraining
Meshes are used to represent complex objects in high fidelity physics simulators across a variety of domains, such as radar sensing and aerodynamics. There is growing interest in using neural networks to accelerate physi…
Graph EmbeddingSensitivityLarge Language Models for Knowledge Graph Embedding Techniques, Methods, and Challenges: A Survey
Large Language Models (LLMs) have attracted a lot of attention in various fields due to their superior performance, aiming to train hundreds of millions or more parameters on large amounts of text data to understand and …
Graph EmbeddingKnowledge Graph EmbeddingSafePowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models
Efficiently solving Optimal Power Flow (OPF) problems in power systems is crucial for operational planning and grid management. There is a growing need for scalable algorithms capable of handling the increasing variabili…
Graph EmbeddingIn-Context LearningManagementStructure-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 PredictionMultimodal Contrastive Representation Learning in Augmented Biomedical Knowledge Graphs
Biomedical Knowledge Graphs (BKGs) integrate diverse datasets to elucidate complex relationships within the biomedical field. Effective link prediction on these graphs can uncover valuable connections, such as potential …
Contrastive LearningGraph EmbeddingKnowledge Graph EmbeddingKnowledge Graphs+2