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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 201–225 of 403 papers

TitleStatusHype
Learning to Embed Categorical Features without Embedding Tables for Recommendation—0
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks—0
Inductive Graph Embeddings through Locality Encodings—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
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
TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrixCode0
Random Walks: A Review of Algorithms and Applications—0
Big Networks: A Survey—0
DINE: A Framework for Deep Incomplete Network Embedding—0
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
SCE: Scalable Network Embedding from Sparsest CutCode0
Online Dynamic Network Embedding—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
Integrated Node Encoder for Labelled Textual Networks—0
CSNE: Conditional Signed Network EmbeddingCode0
Network Embedding Using Deep Robust Nonnegative Matrix Factorization—0
New Datasets and a Benchmark of Document Network Embedding Methods for Scientific Expert FindingCode0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
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