Graph Neural Networks: GNNs excel in understanding social networks by capturing relationships between individuals, predicting links, identifying influential nodes, or detecting communities.
GNNs are used in recommendation engines to model user-item interactions, providing personalized recommendations in e-commerce, streaming platforms, or content suggestions.
GNNs aid in analyzing molecular structures, predicting protein interactions, drug discovery, and understanding biological pathways, contributing significantly to bioinformatics and drug development.
GNNs help detect fraudulent activities by analyzing transaction networks, identifying anomalous patterns, and flagging suspicious behavior in financial transactions or online activities.
Natural Language Processing (NLP):
GNNs are employed to analyze semantic relationships between words, sentences, or documents, enhancing tasks like document classification, sentiment analysis, and entity recognition.
Transportation and Logistics:
GNNs assist in optimizing transportation networks, predicting traffic flow, recommending routes, or optimizing delivery logistics by understanding the relationships between various nodes (e.g., roads, intersections, and traffic patterns).
GNNs have been applied to understanding spatial relationships in images, such as scene graph generation, object detection, and image segmentation tasks.
Recommendation in Knowledge Graphs:
GNNs are used in knowledge graphs to infer missing relations or properties, recommend relevant entities, and perform reasoning over graph-structured data.
Power Grid Optimization:
GNNs help model power grids, predict faults, optimize energy distribution, and improve the resilience and efficiency of electrical networks.
Criminal Network Analysis:
GNNs aid law enforcement agencies in analyzing criminal networks, identifying key players, understanding network structures, and predicting potential criminal activities