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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 361–370 of 403 papers

TitleStatusHype
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Network embedding unveils the hidden interactions in the mammalian virome—0
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks—0
Inductive Graph Embeddings through Locality Encodings—0
Integrated Node Encoder for Labelled Textual Networks—0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
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