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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

Showing 141150 of 403 papers

TitleStatusHype
MUSE: Multi-faceted Attention for Signed Network Embedding0
Network Embedding via Deep Prediction Model0
Mutual Contrastive Learning for Visual Representation LearningCode1
ASBERT: Siamese and Triplet network embedding for open question answering0
Edgeless-GNN: Unsupervised Representation Learning for Edgeless NodesCode0
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network EmbeddingCode0
Dynamic Network Embedding Survey0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Progresses and Challenges in Link Prediction0
Fast Graph Learning with Unique Optimal SolutionsCode1
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