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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 311320 of 403 papers

TitleStatusHype
Global Vectors for Node RepresentationsCode0
Representation Learning for Recommender Systems with Application to the Scientific Literature0
Deep Adversarial Network Alignment0
Unsupervised Network Embedding for Graph Visualization, Clustering and ClassificationCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
Heterogeneous Edge Embeddings for Friend Recommendation0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
Learning Vertex Representations for Bipartite NetworksCode0
Search Efficient Binary Network EmbeddingCode0
Attributed Network Embedding via Subspace DiscoveryCode0
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