Graph Neural Networks (GNNs) have a wide range of applications across diverse fields:
Combinatorial Optimization
GNNs form the foundation of several optimization techniques. They are employed for tasks like determining shortest paths or identifying Eulerian circuits in graphs. Additionally, they enhance human-made algorithms by producing chip configurations superior to those created by humans. Furthermore, GNNs refine expert-designed branch and bound methods by improving decision-making processes.
Social Network Analysis
Due to their ability to model social graphs effectively, GNNs are extensively used to create recommendation systems that consider both social connections and product attributes, enhancing personalized recommendations.
Protein Folding
GNNs are integral to AlphaFold, an AI developed by Google’s DeepMind for predicting protein structures. This technology has consistently excelled in various CASP competitions.
Cybersecurity
By treating computer networks as graphs, GNNs can effectively detect anomalies. Malicious activities within these networks often manifest as anomalies in provenance graphs. The models have been employed to identify such anomalies at various levels — nodes, paths, and edges — to detect malicious behavior comprehensively.
Water Distribution Networks
Water supply systems can be modeled as graphs, making GNNs a natural fit for analyzing such networks. These models have been used for forecasting water demand by integrating data from various District Measuring Areas, thereby improving the accuracy of predictions. Additionally, they aid in creating metamodels that enhance modeling efficiency and provide clearer insights into complex systems.
Each application leverages the unique capabilities of GNNs to solve intricate problems across multiple domains.