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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 331–340 of 403 papers

TitleStatusHype
Binarized Attributed Network EmbeddingCode0
Attention Models with Random Features for Multi-layered Graph Embeddings—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks—0
BiasedWalk: Biased Sampling for Representation Learning on GraphsCode0
Deep Feature Learning of Multi-Network Topology for Node Classification—0
Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
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