What Exactly Are Graph Neural Networks?

In essence, GNNs are neural networks capable of processing graph-structured data. This includes anything from social networks to biological protein-interaction networks to even public transit systems.

Formally, a graph contains nodes (vertices) and edges connecting node pairs. Each node and edge can hold useful feature information. Unlike images or text which have a grid-like structure, graphs can be highly complex and variable. This is where Graph Neural Network comes in.

GNN architectures leverage message-passing algorithms to aggregate neighborhood features for each node. This gives every node a convolved feature representation of its local graph structure. The neural network layers then operate on these learned node embeddings to output relevant predictions.

For instance, GNNs can classify phenotype roles of individual proteins in a biological network based on their connections. The key benefit is explicitly encoding domain structure within the neural network itself.

Types of Graph Neural Networks

There are several types of graph neural networks, each with unique strengths:

1. Graph Convolutional Networks (GCN) GCNs extend CNNs to graph-structured data by aggregating features from neighboring nodes. They use efficient localized filters to scale across large graphs like social or protein networks. Nodes gather broader context with deeper layers, similar to expanding receptive fields in CNNs. Introduced by Kipf & Welling (2017), GCNs enabled large-scale semi-supervised learning on graphs.

2. Graph Attention Networks (GAT) GATs assign different importance to neighbors using attention scores during feature aggregation. This helps capture meaningful relationships, like strong ties in social or biological networks. Multi-head attention enhances expressiveness by focusing on different graph aspects. First proposed by Velickovic et al. (2018), GATs improve flexibility and performance in graph tasks.

3. Graph Recurrent Networks (GRN) GRNs apply RNN concepts to graphs, updating node states using prior neighbor states over time. They suit dynamic graphs like evolving social or transaction networks. "GraphRNN" (You et al., 2018) pioneered graph sequence generation with deep autoregressive models. GRNs capture spatial-temporal dynamics for tasks like traffic flow or molecular simulation.

4. Graph Generator Networks (GGN) GGNs create synthetic graphs similar to real ones using GANs or VAEs. They learn to mimic graph properties like degree distribution and clustering patterns. "GraphGAN" (Wang et al., 2018) introduced adversarial training for realistic graph generation. Applications include data augmentation, anonymization, and generating molecule or network topologies.

Curious how these GNNs are used in real applications like fraud detection, drug discovery, and recommendation engines? Click here to read more.