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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 101–110 of 403 papers

TitleStatusHype
DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks—0
Demographic Inference on Twitter using Recursive Neural Networks—0
AHINE: Adaptive Heterogeneous Information Network Embedding—0
Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models—0
Diffusion Based Network Embedding—0
Diffusion Maps for Textual Network Embedding—0
DINE: A Framework for Deep Incomplete Network Embedding—0
DISCO: Influence Maximization Meets Network Embedding and Deep Learning—0
Associative Learning for Network Embedding—0
Initialization for Network Embedding: A Graph Partition Approach—0
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