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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 341–350 of 403 papers

TitleStatusHype
Learning Deep Network Representations with Adversarially Regularized AutoencodersCode0
NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic NetworksCode0
Hierarchical Reinforcement Learning for Zero-shot Generalization with Subtask DependenciesCode0
Efficient Training on Very Large Corpora via Gramian Estimation—0
Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information NetworksCode0
Resource-Efficient Neural Architect—0
Spectral Network Embedding: A Fast and Scalable Method via Sparsity—0
struc2gauss: Structural Role Preserving Network Embedding via Gaussian Embedding—0
Diffusion Maps for Textual Network Embedding—0
GANE: A Generative Adversarial Network Embedding—0
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