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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 81–90 of 403 papers

TitleStatusHype
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
ExplaiNE: An Approach for Explaining Network Embedding-based Link Predictions—0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation—0
Deep Adversarial Network Alignment—0
A Survey on Signed Graph Embedding: Methods and Applications—0
Diffusion Based Network Embedding—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Deep Contrastive Multiview Network Embedding—0
Demographic Inference on Twitter using Recursive Neural Networks—0
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