SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 351–375 of 403 papers

TitleStatusHype
Diffusion Based Network Embedding—0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
RSDNE: Exploring Relaxed Similarity and Dissimilarity from Completely-imbalanced Labels for Network EmbeddingCode0
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network EmbeddingCode0
Scalable attribute-aware network embedding with locality—0
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information NetworksCode0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length—0
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding—0
Broad Learning for Healthcare—0
Fast Sequence Based Embedding with Diffusion GraphsCode1
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement—0
Can Network Embedding of Distributional Thesaurus be Combined with Word Vectors for Better Representation?—0
Learning Role-based Graph EmbeddingsCode0
Complex Network Classification with Convolutional Neural Network—0
mvn2vec: Preservation and Collaboration in Multi-View Network EmbeddingCode0
Learning Document Embeddings With CNNs—0
SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link PredictionCode0
PRUNE: Preserving Proximity and Global Ranking for Network EmbeddingCode0
Representation Learning for Scale-free Networks—0
Adversarial Network Embedding—0
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization PerspectiveCode0
Vertex-Context Sampling for Weighted Network Embedding—0
Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific DataCode0
Show:102550
← PrevPage 15 of 17Next →

No leaderboard results yet.