11.1 sgnc demo


The pre-built demonstration highlights the capabilities of the package. It is self explanatory.

ml demo sgnc

A sample of the interactions:

StellarGraph for Node Classification

Welcome to a demonstration of node classification in a graph knowledge
structure. StellarGraph is used to represent the graph. The sample
dataset is a well known public network dataset known as Cora. It is
available from linqs.soe.ucsc.edu/data and consists of nodes which are
academic publications and edges that link citations. The nodes have
been classified  into seven subject areas as we will see below.

A Graph Convolution Network (GCN) is used to build a classification
model to predict the subject area of a publication based on the graph
structure. This neural network model uses a graph convolution layer
which uses the graph adjacency matrix to learn about a publication's

This demonstration will prepare the dataset, create the GCN layers,
and then train a model and evaluate its performance.

Press Enter to continue: 

Dataset Description

The dataset, available through the StellarGraph package itself, has
been attached and is ready to be loaded into the StellaGraph data

The Cora dataset consists of 2708 scientific publications classified
into one of seven classes. The citation network consists of 5429
links. Each publication in the dataset is described by a 0/1-valued
word vector indicating the absence/presence of the corresponding word
from the dictionary. The dictionary consists of 1433 unique words.


Machine Learning Model

A model is now being built. This is the Graph Convolution Network
model. For a small dataset it takes a few seconds.

Using GCN (local pooling) filters...


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