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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 301–310 of 403 papers

TitleStatusHype
Deep Hashing for Signed Social Network Embedding—0
Deep Kernel Supervised Hashing for Node Classification in Structural Networks—0
Deep Learning for Learning Graph Representations—0
Deep Partial Multiplex Network Embedding—0
Selective Network Discovery via Deep Reinforcement Learning on Embedded Spaces—0
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Demographic Inference on Twitter using Recursive Neural Networks—0
Detecting local perturbations of networks in a latent hyperbolic embedding space—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Diffusion Based Network Embedding—0
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