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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 201–225 of 403 papers

TitleStatusHype
Pay Attention to Relations: Multi-embeddings for Attributed Multiplex Networks—0
Physiological Signal Embeddings (PHASE) via Interpretable Stacked Models—0
PPPNE: Personalized proximity preserved network embedding—0
Predict Anchor Links across Social Networks via an Embedding Approach—0
Privacy Attacks on Network Embeddings—0
Progresses and Challenges in Link Prediction—0
QUINT: Node embedding using network hashing—0
Random Walks: A Review of Algorithms and Applications—0
Range-Only Localization in n-Dimensional Networks With Arbitrary Anchor Placement—0
Recommending on graphs: a comprehensive review from a data perspective—0
REFINE: Random RangE FInder for Network Embedding—0
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Relation Structure-Aware Heterogeneous Information Network Embedding—0
Representation Learning for Recommender Systems with Application to the Scientific Literature—0
Representation Learning for Scale-free Networks—0
Resource-Efficient Neural Architect—0
Network Representation of Large-Scale Heterogeneous RNA Sequences with Integration of Diverse Multi-omics, Interactions, and Annotations Data—0
RNE: A Scalable Network Embedding for Billion-scale Recommendation—0
Scalable attribute-aware network embedding with locality—0
Scalable Hierarchical Embeddings of Complex Networks—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
Semantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semi-supervised Network Embedding with Differentiable Deep Quantisation—0
SepNE: Bringing Separability to Network Embedding—0
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