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 citations. 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 structures. 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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