Go to TogaWare.com Home Page. Data Science Desktop Survival Guide
by Graham Williams
Duck Duck Go


Graph Embedding

A graph embedding learns a mapping from the graph to a more traditional vector space as utilised by data scientists, while preserving relevant network properties. Vector operations tend to be simpler and faster than the same operations on graphs. Traditional machine learning and statistics tend to operate in vector spaces.

A typical example is nearest neighbours. Within a graph space links can be traversed from node to node as we travel further from a node relationships become less meaningful. On transforming to vector space distance metrics can be used over the features for a more straightforward nearest neighbours.

See “Graph Embedding Techniques, Applications, and Performance: A Survey” from https://arxiv.org/abs/1705.02801.

Support further development by purchasing the PDF version of the book.
Other online resources include the GNU/Linux Desktop Survival Guide.
Books available on Amazon include Data Mining with Rattle and Essentials of Data Science.
Popular open source software includes rattle and wajig.
Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 2000-2020 Togaware Pty Ltd. . Creative Commons ShareAlike V4.