On the Power of Graph Neural Networks and Feature Augmentation
  Strategies to Classify Social Networks
Walid Guettala László Gulyás
This paper studies four Graph Neural Network architectures (GNNs) for a graph
classification task on a synthetic dataset created using classic generative
models of Network Science. Since the synthetic networks do not contain (node or
edge) features, five different augmentation strategies (artificial feature
types) are applied to nodes. All combinations of the 4 GNNs (GCN with
Hierarchical and Global aggregation, GIN and GATv2) and the 5 feature types
(constant 1, noise, degree, normalized degree and ID -- a vector of the number
of cycles of various lengths) are studied and their performances compared as a
function of the hidden dimension of artificial neural networks used in the
GNNs. The generalisation ability of these models is also analysed using a
second synthetic network dataset (containing networks of different sizes).Our
results point towards the balanced importance of the computational power of the
GNN architecture and the the information level provided by the artificial
features. GNN architectures with higher computational power, like GIN and
GATv2, perform well for most augmentation strategies. On the other hand,
artificial features with higher information content, like ID or degree, not
only consistently outperform other augmentation strategies, but can also help
GNN architectures with lower computational power to achieve good performance.