Recent neural network advancements have enhanced research methods in data mining and pattern recognition. Various machine learning tasks, including object identification, translation, speech recognition, can help researchers streamline deep learning paradigms, including RNN, CNN, and Auto-Encoders.

Graph neural networks are an advanced, cutting-edge approach applied to the graph. These networks provide a streamlined way to perform edge-level, node-level, and graph-level predictions. Graph neural networks have transformed the digital world, data mining, and visualization techniques. Here are a few practical applications of these networks. Read on!

Prediction of Medications’ Side Effects

Side effects caused by medications increase hospitalizations worldwide, including in North American countries. The number of elderly people ending up in emergency rooms due to adverse side effects is higher than young adults. It has become a significant problem in the U.S and Canada. 

Graph neural networks and related methodologies allow researchers in the pharmaceutical sector to test different combinations of medicines and predict their side effects. Researchers and doctors collaborate to review patients’ medical history and evidence-based research studies to measure the risk of adverse reactions. 

Thanks to Graph neural networks, including classification and similarity algorithms, research can determine the risk of drugs in a diverse population and develop personalized medicines to mitigate side effects. 

For example, a convolutional graph network is an advanced method for developing state-of-the-art predictive models and gaining insights into drug-to-drug interactions and their side effects. Although there is a need for more research, the results of graph neural networks as drug side effects predictors are promising. 

Knowledge Graphs for Product Recommendations 

Product recommendations are an integral part of online businesses, including eCommerce sites, allowing them to suggest products to buyers based on their interests. For example, Amazon, a popular online marketplace, uses knowledge graphs to identify and analyze relationships between product data and customers’ interests. 

Companies can use downstream applications to answer users’ questions, solve their queries automatically, and recommend products to increase sales and generate higher returns on investments (ROIs). Amazon has developed a graph neural network known as GENI, an advanced tool to streamline the process of product recommendations. 

Improved Physical Intuition for Computer Systems 

Deep learning has improved computer vision and other capabilities of systems, including image classification, object detection and analysis, pixel segmentation, and data analysis. However, there is a lack of connection between computer vision and physical intuition. 

So, this is where graph neural networks come into play. The technology offers numerous algorithms and tools to empower machines with a human-like physical intuition. The purpose is to develop predictive models and allow machines to determine what may happen in the future based on existing data. 

For example, CNN is a cutting-edge approach in graph neural networks with powerful object differentiation capabilities in scenes. It uses interactive networks to identify relationships between different objects in a picture or video. Further development in graph neural networks will completely transform the physical intuition of computer vision and machines.