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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 151–160 of 403 papers

TitleStatusHype
Collaborative filtering via heterogeneous neural networks—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
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