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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

Showing 381390 of 403 papers

TitleStatusHype
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via RankingCode0
DeBayes: a Bayesian Method for Debiasing Network EmbeddingsCode0
Simultaneous Weight and Architecture Optimization for Neural NetworksCode0
BHGNN-RT: Network embedding for directed heterogeneous graphsCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
metapath2vec: Scalable Representation Learning for Heterogeneous NetworksCode0
TextCNN with Attention for Text ClassificationCode0
Data driven approximation of parametrized PDEs by Reduced Basis and Neural NetworksCode0
DANES: Deep Neural Network Ensemble Architecture for Social and Textual Context-aware Fake News DetectionCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
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