Summary

A lot of real-world data can be naturally represented as graphs. Graphs are especially important in a social network context where multiple entities (users, posts, or media) are linked together, forming natural graphs. In recent times, the spread of misinformation and fake news is a problem of growing concern. This chapter focused on detecting fake news using GNNs.

We began by first learning some basic concepts about graphs and techniques to learn on graphs. This included using static features extracted from graph analytics (such as degrees and path lengths), node and graph embeddings, and finally, neural message passing, using GNNs. We looked at the UPFD framework and how a graph can be built for a news article, complete with node features that incorporate historical user behavior. Finally, we trained a GNN model to build a graph classifier that detects whether a news article is fake or not.

In the field of cybersecurity, graphs are especially important. This is because...