| Feature |
Spektral |
Stellargraph |
Graph Nets |
| Tutorials availability |
There’re about 20 examples that show different usages. |
A lot of well documented demos in python notebooks. |
Very few examples |
| Year of creation |
2020 |
2018 |
2018 |
| Paper |
Spectral Clustering with Graph Neural Networks for Graph Pooling cited less than 100 times |
No paper |
Relational inductive biases, deep learning, and graph networks cited more than 1400 times |
| Author/s |
danielegrattarola
|
CSIRO’s Data61 |
DeepMind |
| Supports |
Tensorflow |
Tensorflow |
Tensorflow and Sonnet |
| Input type |
Simple datasets of multiple thousands single numerical value nodes |
Dataframes |
In the examples the node values were required to be arrays of float values. It’s also possible to choose whether to make a graph without edges, nodes without values, or edges without directions. |
| Usage |
Examples show a case of citations network, molecular prediction model, and graph network classification. |
Varied use cases of supervised and unsupervised learning. It also supports 20 different types of algorithms. |
Examples show one physical example, one about the shortest path problem solution and another simplified demo. |
| Node prediction |
Yes |
Classification |
Doesn’t predict graphs, nodes or edges according to the manual, but predicts states, shortest paths and other operations that are done over an existing graph. |
| Edge prediction |
Not in the examples |
Yes (link prediction) |
| Graph prediction |
Yes |
Classification |
| Support categorical data |
|
|
Yes, in one hot vector. |
