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Network Embedding

Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction

Source: Tutorial on NLP-Inspired Network Embedding

Papers

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Showing 176–200 of 403 papers

TitleStatusHype
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Layer-stacked Attention for Heterogeneous Network Embedding—0
Boosting House Price Predictions using Geo-Spatial Network EmbeddingCode0
Adversarial Privacy Preserving Graph Embedding against Inference AttackCode1
MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approachCode1
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
DINE: A Framework for Deep Incomplete Network Embedding—0
Big Networks: A Survey—0
Random Walks: A Review of Algorithms and Applications—0
GloDyNE: Global Topology Preserving Dynamic Network EmbeddingCode1
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Next Waves in Veridical Network Embedding—0
Network Embedding with Completely-imbalanced LabelsCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Online Dynamic Network Embedding—0
SCE: Scalable Network Embedding from Sparsest CutCode0
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
Network Together: Node Classification via Cross-Network Deep Network EmbeddingCode1
Integrated Node Encoder for Labelled Textual Networks—0
CSNE: Conditional Signed Network EmbeddingCode0
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