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 151–175 of 403 papers

TitleStatusHype
Compositional Network Embedding—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Exact Recovery of Community Structures Using DeepWalk and Node2vec—0
HONEM: Learning Embedding for Higher Order Networks—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Controlled Deep Reinforcement Learning for Optimized Slice Placement—0
Collaborative filtering via heterogeneous neural networks—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
COSINE: Compressive Network Embedding on Large-scale Information Networks—0
An Out-of-the-box Full-network Embedding for Convolutional Neural Networks—0
Cross Version Defect Prediction with Class Dependency Embeddings—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks—0
Equivalence between LINE and Matrix Factorization—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph-Level Embedding for Time-Evolving Graphs—0
CoarSAS2hvec: Heterogeneous Information Network Embedding with Balanced Network Sampling—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Data-driven biological network alignment that uses topological, sequence, and functional information—0
Inductive Graph Embeddings through Locality Encodings—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
Show:102550
← PrevPage 7 of 17Next →

No leaderboard results yet.