How to Build a Graph-based Neural Network for Anomaly Detection in 6 Steps

Learn to build a Graph Convolutional Network that can handle heterogeneous graph data for link prediction

Image from Pixabay

This article is a detailed technical deep dive into how to build a powerful model for anomaly detection with graph data containing entities of different types (heterogeneous graph data).

The model you will learn about is based on the paper titled “Interaction-Focused Anomaly Detection on Bipartite Node-and-Edge-Attributed Graphs” presented by Grab, an Asian tech company, at the 2023 International Joint Conference on Neural Networks (IJCNN) conference.

This Graph Convolutional Network (GCN) model can handle heterogeneous graph data, meaning that nodes and edges are of different types. These graphs are structurally complex as they represent relationships between different types of entities or nodes.

GCNs that can handle heterogeneous graph data is an active area of research. The convolutional operations in the model have been adapted to address challenges around handling different node types and their relationships in a heterogeneous graph.

In contrast, homogeneous graphs involve nodes and edges of the same type. This type of graph is structurally…