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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 61–70 of 403 papers

TitleStatusHype
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
ToupleGDD: A Fine-Designed Solution of Influence Maximization by Deep Reinforcement LearningCode1
Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning—0
Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypes—0
Cross-Network Social User Embedding with Hybrid Differential Privacy GuaranteesCode0
Associative Learning for Network Embedding—0
Multiplex Heterogeneous Graph Convolutional NetworkCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
Signed Network Embedding with Application to Simultaneous Detection of Communities and Anomalies—0
Large-Scale Privacy-Preserving Network Embedding against Private Link Inference Attacks—0
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