Graph Embedding
477 papers with code • 1 benchmarks • 11 datasets
Graph embeddings learn a mapping from a network to a vector space, while preserving relevant network properties.
( Image credit: GAT )
Benchmarks
These leaderboards are used to track progress in Graph Embedding
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Synthetic Dynamic Networks
Subtasks
Latest papers with no code
TPLLM: A Traffic Prediction Framework Based on Pretrained Large Language Models
Traffic prediction constitutes a pivotal facet within the purview of Intelligent Transportation Systems (ITS), and the attainment of highly precise predictions holds profound significance for efficacious traffic management.
PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems
PowerFlowMultiNet outperforms traditional methods and other deep learning approaches in terms of accuracy and computational speed.
Negative Sampling in Knowledge Graph Representation Learning: A Review
This comprehensive survey paper systematically reviews various negative sampling (NS) methods and their contributions to the success of KGRL.
EntailE: Introducing Textual Entailment in Commonsense Knowledge Graph Completion
In this paper, we propose to adopt textual entailment to find implicit entailment relations between CSKG nodes, to effectively densify the subgraph connecting nodes within the same conceptual class, which indicates a similar level of plausibility.
SAGMAN: Stability Analysis of Graph Neural Networks on the Manifolds
Modern graph neural networks (GNNs) can be sensitive to changes in the input graph structure and node features, potentially resulting in unpredictable behavior and degraded performance.
MQuinE: a cure for "Z-paradox" in knowledge graph embedding models
Knowledge graph embedding (KGE) models achieved state-of-the-art results on many knowledge graph tasks including link prediction and information retrieval.
Spoofing Detection in the Physical Layer with Graph Neural Networks
In a spoofing attack, a malicious actor impersonates a legitimate user to access or manipulate data without authorization.
Edge-Enabled Anomaly Detection and Information Completion for Social Network Knowledge Graphs
Firstly, we introduce a lightweight distributed knowledge graph completion architecture that utilizes knowledge graph embedding for data analysis.
Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding
Representing graph data in a low-dimensional space for subsequent tasks is the purpose of attributed graph embedding.
An FPGA-Based Accelerator for Graph Embedding using Sequential Training Algorithm
A graph embedding is an emerging approach that can represent a graph structure with a fixed-length low-dimensional vector.
