There are broad-spectrum real-world applications that can be powered by graph technologies. Social networks dwell on graphs that best model how people follow and befriend each other; Biotech and pharmaceutical companies leverage graphs to understand protein interactions and chemical compounds efficacies; Supply chains, telco networks, and power grids are naturally presented as graphs for root-cause analysis; Financial transactions naturally form networks and fraud detections, smart recommendations or asset-liability management are possible to run across these networked data for improved productivity, prediction accuracy or business intelligence. Many industries are looking to graph’s smart (and deep) data processing capabilities to help with their businesses.
The big-data era started around 2010, as more and more industries are interested in machine learning (and deep learning and AI) to boost their business predictability; some have been using deep learning and specifically varied neural networks to extract more predictive powers. There are three major problems lingering around, though: