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 341–350 of 403 papers

TitleStatusHype
Multi-Level Network Embedding with Boosted Low-Rank Matrix ApproximationCode0
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention—0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Resource-Efficient Neural Architect—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
Show:102550
← PrevPage 35 of 41Next →

No leaderboard results yet.