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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
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
struc2vec: Learning Node Representations from Structural IdentityCode0
Structural Deep Network EmbeddingCode0
NetSMF: Large-Scale Network Embedding as Sparse Matrix FactorizationCode0
Graph Representation Learning via Hard and Channel-Wise Attention NetworksCode0
GraphVite: A High-Performance CPU-GPU Hybrid System for Node EmbeddingCode0
H^2TNE: Temporal Heterogeneous Information Network Embedding in Hyperbolic SpacesCode0
HAHE: Hierarchical Attentive Heterogeneous Information Network EmbeddingCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Attributed Network Embedding via Subspace DiscoveryCode0
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