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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
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
GENE: Global Event Network EmbeddingCode0
Name Disambiguation in Anonymized Graphs using Network EmbeddingCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
RiWalk: Fast Structural Node Embedding via Role IdentificationCode0
Global Vectors for Node RepresentationsCode0
Enhanced Network Embedding with Text InformationCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
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
RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network EmbeddingCode0
RWNE: A Scalable Random-Walk-Based Network Embedding Framework with Personalized Higher-Order Proximity PreservedCode0
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vecCode0
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node DescriptorsCode0
Network Embedding Exploration Tool (NEExT)Code0
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