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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

Showing 7180 of 403 papers

TitleStatusHype
Dynamic Embedding on Textual Networks via a Gaussian ProcessCode0
Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data ManifoldsCode0
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network EmbeddingCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Attributed Network Embedding for Incomplete Attributed NetworksCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Flexible Attributed Network EmbeddingCode0
Font Size: Community Preserving Network EmbeddingCode0
Adversarial Attack on Network Embeddings via Supervised Network PoisoningCode0
A Simple and Powerful Framework for Stable Dynamic Network EmbeddingCode0
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