# Graph Neural Networks

Last week I was looking just a little about the Complex Networks research field, which consists of learning/extracting information from big graphs (in a computer structure sense). Recently several Deep Learning algorithms have gain popularity, in particular the Graph Neural Networks (GNN). Most of the work is focused on how to represent the graph for using Deep Learning.

Here there are some useful links for expand the topic:

- Deep walk, an algorithm based on Random Walk for Latent representation (embedings) on graphs.
- Graph Neural Networks models, (Scarcelli et al 2009) one of the first article about the topic.
- A Medium article about machine learning on graph structures.
- Another Medium article about Graph Convolutional Networks (GCN)
- The Stellargraph library (python) were several of these algorithms are implemented (in conjunction with TF)