Learn how graph algorithms can help you predict real-world behavior and why an averages approach fails to describe group dynamics. Graphs provide a method to truly store and analyze information with the relationships that connect the data. Algorithms further deepen our understanding of data through aggregation and perspectives to help developers make accurate and valuable business decisions for the future based on existing scenarios.
Amy Hodler and William Lyon provide an overview of which algorithms to apply for various types of optimal paths, influence in a network, and community detection. They outline use cases that span across industries including recommendations, resiliency planning, fraud prevention, and traffic engineering and routing (such as IP and call). This all comes alive for you through a live demo, where you’ll see what kinds of information you can retrieve and decisions you can make based on results from different algorithms and sets of data.
Amy E. Hodler is a network science devotee and AI and graph analytics program manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray. Amy has a love for science and art with a fascination for complexity studies and graph theory. She tweets as @amyhodler.
William Lyon is a software engineer on the developer relations team at Neo4j, where he works primarily on integrating the Neo4j graph database with other technologies. Previously, William was a software developer for several startups in the real estate, quantitative finance, and predictive API spaces. William holds a master’s degree from the University of Montana.
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