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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 351360 of 403 papers

TitleStatusHype
Dynamic Network Embedding via Incremental Skip-gram with Negative SamplingCode0
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph EmbeddingCode0
Is a Single Vector Enough? Exploring Node Polysemy for Network EmbeddingCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Task-Guided Pair Embedding in Heterogeneous NetworkCode0
JNET: Learning User Representations via Joint Network Embedding and Topic EmbeddingCode0
DyCSC: Modeling the Evolutionary Process of Dynamic Networks Based on Cluster StructureCode0
Billion-scale Network Embedding with Iterative Random ProjectionCode0
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for FontsCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
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