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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 161–170 of 403 papers

TitleStatusHype
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
Global Vectors for Node RepresentationsCode0
Efficient Inner Product Approximation in Hybrid Spaces—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Initialization for Network Embedding: A Graph Partition Approach—0
dynnode2vec: Scalable Dynamic Network Embedding—0
A Multi-Semantic Metapath Model for Large Scale Heterogeneous Network Representation Learning—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
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