Graph Embedding
473 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 |
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Libraries
Use these libraries to find Graph Embedding models and implementationsDatasets
BioGRID
LastFM Asia
OLPBENCH
HeriGraph
Synthetic Dynamic Networks
Subtasks
Latest papers
Mitigating Heterogeneity among Factor Tensors via Lie Group Manifolds for Tensor Decomposition Based Temporal Knowledge Graph Embedding
dellixx/tkbc-lie
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Recent studies have highlighted the effectiveness of tensor decomposition methods in the Temporal Knowledge Graphs Embedding (TKGE) task.
scCDCG: Efficient Deep Structural Clustering for single-cell RNA-seq via Deep Cut-informed Graph Embedding
Addressing these limitations, we introduce scCDCG (single-cell RNA-seq Clustering via Deep Cut-informed Graph), a novel framework designed for efficient and accurate clustering of scRNA-seq data that simultaneously utilizes intercellular high-order structural information.
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding
marcob46/mpxgat
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Graph representation learning has rapidly emerged as a pivotal field of study.
TESTAM: A Time-Enhanced Spatio-Temporal Attention Model with Mixture of Experts
hyunwookl/testam
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In this paper, we propose a novel deep learning model named TESTAM, which individually models recurring and non-recurring traffic patterns by a mixture-of-experts model with three experts on temporal modeling, spatio-temporal modeling with static graph, and dynamic spatio-temporal dependency modeling with dynamic graph.
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
renj-xu/negsc
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To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
PreRoutGNN for Timing Prediction with Order Preserving Partition: Global Circuit Pre-training, Local Delay Learning and Attentional Cell Modeling
First, we propose global circuit training to pre-train a graph auto-encoder that learns the global graph embedding from circuit netlist.
DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations
To obtain a more comprehensive embedding representation of nodes, a novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced.
GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
wendig/gd-caf
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In particular, we introduce Graph Dual-stream Convolutional Attention Fusion (GD-CAF), a novel approach designed to learn from historical spatiotemporal graph of precipitation maps and nowcast future time step ahead precipitation at different spatial locations.
Temporal Link Prediction Using Graph Embedding Dynamics
Traditional approaches to temporal link prediction have focused on finding the aggregation of dynamics of the network as a unified output.
Block-Diagonal Orthogonal Relation and Matrix Entity for Knowledge Graph Embedding
The primary aim of Knowledge Graph embeddings (KGE) is to learn low-dimensional representations of entities and relations for predicting missing facts.
