One of the most fascinating concepts to emerge from Facebook was the social graph. Basically, social graphs mapped the user’s connections. The output was a graph defining and illustrating the relationships between the users, their connections, their connections’ connections, and more. Visualization of the graph along with its implications have been a great source of amusement.
Think about the computing power it took to render the graph of each of the users on Facebook. That is a massive amount of power. The immense amount of user data requires great responsibility and discretion. For privacy reasons, Facebook has now limited the access to the social graph by its users.
The elegance of the social graph and the public’s familiarity with it make it a great opportunity for the use of Graph Neural Networks (GNNs). The original machine learning killer app was Convolutional Neural Networks, or CNNs, which helped to identify which handwritten squiggles were which letters of the alphabet and which YouTube videos showed cats. Neural networks have since been applied to other data types and structures besides images. GNNs apply neural network algorithms to the nodes and edges of graph data to open up new possibilities for what we can do with graph-based data.
The amount of information these sophisticated algorithms absorb creates deeper, richer information that will help businesses stay competitive and make better decisions under uncertainty. These artificial neural networks are proving useful in diverse use cases and business and government applications.
Typical GNN activities include:
- Node classification to fill gaps with new information based on the newly acquired unstructured data. This allows for spotting disruptors to your social network.
- Edge prediction, which adds value by discovering new information between connections. For example, predicting new relationships such as users who might soon become friends.
- Discovering clusters of group members who share, or are likely to share, otherwise inconspicuous common interests or behaviors despite having no direct connections between them. Clustering is often used to predict market conditions, movie recommendations, and money laundering.
Competition has increased pressure for timely insights. Let Katana Graph ease the pressure. Let’s connect!
