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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 4150 of 403 papers

TitleStatusHype
LouvainNE: Hierarchical Louvain Method for High Quality and Scalable Network Embedding.Code1
A Survey on Role-Oriented Network EmbeddingCode1
Representation Learning for Attributed Multiplex Heterogeneous NetworkCode1
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNNCode1
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional NetworksCode1
Multiplex Heterogeneous Graph Convolutional NetworkCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic SpaceCode1
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network EmbeddingCode1
Unsupervised Differentiable Multi-aspect Network EmbeddingCode1
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