Kernel Node Embeddings

Motivation

KernelNE is a method for learning node embeddings by incorporating kernel functions with models relying on weighted matrix factorization, encoding random walk-based structural information of the graph.

Code

An implementation of the project in C++ can be reached at the Github repository.

Contributors

Abdulkadir Ƈelikkanat and Fragkiskos D. Malliaros

References

A. Celikkanat and F. D. Malliaros, Kernel Node Embeddings, 7th IEEE Global Conference on Signal and Information Processing (GlobalSIP), 2019