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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 326–350 of 403 papers

TitleStatusHype
Embedding Node Structural Role Identity into Hyperbolic Space—0
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—0
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering—0
End-to-End triplet loss based fine-tuning for network embedding in effective PII detection—0
EPARS: Early Prediction of At-risk Students with Online and Offline Learning Behaviors—0
EPINE: Enhanced Proximity Information Network Embedding—0
EPNE: Evolutionary Pattern Preserving Network Embedding—0
Equivalence between LINE and Matrix Factorization—0
EvalNE: A Framework for Evaluating Network Embeddings on Link Prediction—0
Explainable, Stable, and Scalable Graph Convolutional Networks for Learning Graph Representation—0
FONDUE: A Framework for Node Disambiguation Using Network Embeddings—0
On Proximity and Structural Role-based Embeddings in Networks: Misconceptions, Techniques, and Applications—0
Full-Network Embedding in a Multimodal Embedding Pipeline—0
Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognition—0
GAHNE: Graph-Aggregated Heterogeneous Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks—0
Learning Features of Network Structures Using Graphlets—0
Grammar-Based Grounded Lexicon Learning—0
Unsupervised Graph Embedding via Adaptive Graph Learning—0
Graph-Level Embedding for Time-Evolving Graphs—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Heterogeneous Edge Embeddings for Friend Recommendation—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
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