What is Graph Neural Network?

A GNN learns by passing messages between connected nodes in the graph and aggregating this information to learn context-aware node, edge, or whole-graph representations.

Core steps in a GNN layer:

1. Message Passing: Each node receives information from its neighbors.

2. Aggregation: Information from neighbors is combined (sum, mean, max, attention).

3. Update: Node’s own representation is updated based on the aggregated info.

After several such layers, each node’s representation contains information about its multi-hop neighborhood in the graph.

3. Why use GNNs instead of normal neural networks?

Traditional models like CNNs and RNNs work well for grids (images) or sequences (text, audio), but many real-world problems are irregular and relational, where the number of connections varies for each element — graphs capture this naturally.

4. Applications of GNNs in AI

GNNs are extremely flexible and are being used in many AI fields:

a) Social Network Analysis

• Predicting friend recommendations (link prediction).

• Detecting fake accounts or fraud by analyzing suspicious connection patterns.

b) Recommendation Systems

• Understanding complex relationships between users and items (e.g., YouTube video recommendations using user-item graphs).

c) Drug Discovery & Bioinformatics

• Modeling molecules as graphs of atoms (nodes) and chemical bonds (edges).

• Predicting molecular properties or potential drug interactions.

d) Knowledge Graphs

• Using GNNs to reason over large knowledge bases for better question answering in AI assistants.

e) Traffic and Transportation

• Predicting traffic flow where intersections = nodes, roads = edges.

f) Cybersecurity

• Analyzing device connection graphs to detect intrusions or malicious activity.

g) Computer Vision

• Scene graph generation (understanding object relationships in an image).

5. Example: AI Application – Fraud Detection

Imagine a banking network:

• Nodes: Customers, transactions, merchants.

• Edges: “Customer made a transaction at merchant.”

• Goal: Predict whether a transaction is fraudulent.

A GNN can:

• Aggregate suspicious patterns from neighboring transactions.

• Learn representations that capture both local anomalies and network-wide patterns.

If you want, I can prepare a clear diagram of how GNNs process graph data step-by-step, so it’s easy to visualize the message passing and aggregation concepts. That would make the idea click instantly.