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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 371380 of 403 papers

TitleStatusHype
Temporal Network Representation Learning via Historical Neighborhoods AggregationCode0
ProNE: Fast and Scalable Network Representation LearningCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Learning Role-based Graph EmbeddingsCode0
Domain-adversarial Network AlignmentCode0
Learning Vertex Representations for Bipartite NetworksCode0
Deep Node Ranking for Neuro-symbolic Structural Node Embedding and ClassificationCode0
LNEMLC: Label Network Embeddings for Multi-Label ClassificationCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
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