On the Power of Graph Neural Networks and Feature Augmentation Strategies to Classify Social Networks
This work addresses the problem of graph classification in social networks for researchers, but it is incremental as it focuses on synthetic data and known methods.
The paper investigates the performance of four Graph Neural Network architectures combined with five feature augmentation strategies for classifying synthetic social networks, finding that both the computational power of the GNN and the information content of the features are crucial, with GIN and GATv2 performing well across most strategies and features like ID or degree consistently outperforming others.
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.