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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 101–125 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
Associative Learning for 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
EPNE: Evolutionary Pattern Preserving Network Embedding—0
Document Network Embedding: Coping for Missing Content and Missing Links—0
Document Network Projection in Pretrained Word Embedding Space—0
BHIN2vec: Balancing the Type of Relation in Heterogeneous Information Network—0
A Multi-Domain VNE Algorithm based on Load Balancing in the IoT networks—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Dynamic Network Embedding Survey—0
Big Networks: A Survey—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
A Block-based Generative Model for Attributed Networks Embedding—0
GANE: A Generative Adversarial Network Embedding—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
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