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 176–200 of 403 papers

TitleStatusHype
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Information Network Embedding for Meta Path based Proximity—0
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
HeteSpaceyWalk: A Heterogeneous Spacey Random Walk for Heterogeneous Information Network Embedding—0
Hierarchical Graph Neural Networks—0
Attribute2vec: Deep Network Embedding Through Multi-Filtering GCN—0
High-order joint embedding for multi-level link prediction—0
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding—0
Clustering Molecular Energy Landscapes by Adaptive Network Embedding—0
Homogeneous Network Embedding for Massive Graphs via Reweighted Personalized PageRank—0
HONEM: Learning Embedding for Higher Order Networks—0
Hyperbolic Multiplex Network Embedding with Maps of Random Walk—0
Hyperbolic Node Embedding for Signed Networks—0
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction—0
Identity-sensitive Word Embedding through Heterogeneous Networks—0
Integrated Node Encoder for Labelled Textual Networks—0
Improved Deep Embeddings for Inferencing with Multi-Layered Networks—0
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment—0
Improving Skip-Gram based Graph Embeddings via Centrality-Weighted Sampling—0
Improving Textual Network Embedding with Global Attention via Optimal Transport—0
Aligning Users Across Social Networks Using Network Embedding—0
Network embedding unveils the hidden interactions in the mammalian virome—0
Hedging carbon risk with a network approach—0
Show:102550
← PrevPage 8 of 17Next →

No leaderboard results yet.