Graph Analytics
Link: http://www.forbes.com/sites/emc/2014/03/14/how-can-graph-analytics-uncover-valuable-insights-about-data/
Graphs can be used to model many types of relationships and can offer insight that would otherwise be invisible to the human eye. Graph Theory and Analytics have applications in many fields. In the field of computer science, graphs are used to represent data organization, the flow of computation, and more such as the link structure of the World Wide Web. It also has applications in sociology, government intelligence, life sciences, manufacturing, and much more. Not only do graphs have applications in almost all fields of research and study, but there are many ways in which a graph can be analyzed to yield useful information. They can be used to identify the strength of relationships and communities, analyze paths, and search for patterns. Networks can be also used to teach artificial intelligence through the use of neural networks. One of the most impressive applications of Graph Analytics is the Google Knowledge Graph, which is a knowledge base meant to improve the Google search engine. The goal of this project is to attempt to better understand and answer queries through the use of data collected about the consumer. There is such a vast amount of data in social media an other applications that humans cannot hope to process such data, and Graph Analytics are used to yield useful information. The applications and possibilities of Graph Theory and Graph Analytics are endless, and they allow humans to make deductions that would otherwise be impossible to notice given such a large volume of data.
Graphs have had a large role in this class thus far. They are used to display and analyze networks of all kinds. They are how we most often represent, visualize, and analyze a network. They are used to analyze strengths of relationships with strong/weak ties. They were used to matching markets with Bipartite Graphs. And most recently, they are used to display the link structure of the World Wide Web, and visualize concepts such as Hubs and Authorities. The Graph structure of the World Wide Web also influences PageRank, and based on how links are pointed and structured determines how easy it is to reach a page and where it will appear in a search engine query. Graph Analytics will always be useful for interpreting and understand network data. We will continue to see amazing projects like the Google Knowledge Graph as we progress, and how far we can go with Graph Theory and Graph Analytics is limitless.