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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 301–325 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
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
Document Network Embedding: Coping for Missing Content and Missing Links—0
Document Network Projection in Pretrained Word Embedding Space—0
Dynamic Graph Embedding via LSTM History Tracking—0
Dynamic Network Embeddings for Network Evolution Analysis—0
Dynamic Network Embedding Survey—0
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
Initialization for Network Embedding: A Graph Partition Approach—0
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks—0
Efficient Inner Product Approximation in Hybrid Spaces—0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path—0
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