Graph Neural Networks in Action

Price: $59.99
(as of Dec 28,2024 23:53:10 UTC – Details)

Publisher ‏ : ‎ Manning (February 25, 2025)
Language ‏ : ‎ English
Paperback ‏ : ‎ 390 pages
ISBN-10 ‏ : ‎ 1617299057
ISBN-13 ‏ : ‎ 978-1617299056
Item Weight ‏ : ‎ 14.8 ounces
Dimensions ‏ : ‎ 7.38 x 0.88 x 9.25 inches

Graph Neural Networks (GNNs) are a powerful tool for analyzing and learning from graph-structured data. From social networks to molecular structures, GNNs have been successfully applied in a wide range of domains to extract valuable insights and make predictions.

In this post, we will explore some real-world examples of GNNs in action.

1. Social Network Analysis: GNNs have been used to model and analyze social networks, uncovering patterns and relationships between individuals. By learning from the graph structure of social connections, GNNs can predict user behavior, identify influential users, and detect communities within the network.

2. Drug Discovery: In the field of pharmaceuticals, GNNs have revolutionized the process of drug discovery. By representing molecules as graphs and leveraging GNNs to predict molecular properties, researchers can accelerate the development of new drugs and identify potential candidates for further exploration.

3. Recommendation Systems: GNNs are also used in recommendation systems to personalize content and make accurate predictions based on user interactions. By modeling user-item interactions as a graph, GNNs can learn from the underlying relationships between users and items to suggest relevant recommendations.

4. Fraud Detection: GNNs have been applied in fraud detection to identify suspicious activities and patterns within financial transactions. By analyzing the graph structure of transaction networks, GNNs can detect anomalies and flag potentially fraudulent behavior in real-time.

Overall, Graph Neural Networks have shown great promise in various applications and continue to push the boundaries of what is possible in graph-based machine learning. With their ability to capture complex relationships and dependencies in graph data, GNNs are a valuable tool for solving challenging problems across different domains.
#Graph #Neural #Networks #Action,gnn