BEGIN:VCALENDAR VERSION:2.0 PRODID:-//hacksw/handcal//NONSGML v1.0//EN METHOD:PUBLISH BEGIN:VEVENT DTSTAMP:20240328T180317Z DESCRIPTION:Click for Latest Location Information: http://graphorum2019.dat aversity.net/sessionPop.cfm?confid=132&proposalid=10835\n
Unsupervised ma chine learning on big graph data solves many important problems. PageRank l ooks for the most influential or popular members of a particular group. Ano ther example is Community Detection, which looks to establish groups of den sely interconnected entities, such as a group of doctors serving a clu ster of patients for a particular condition such as diabetes or opioid addi ction.
\nOn the other hand, supervised machine learning – such as classification of entities using features derived from the en tity relationship graph – can achieve unforeseeable accuracy. In this talk, we share real case studies on both supervised and unsupervised machine learning using a big graph. We will also provide a look at a graph algorithm library open to anyone with real graph data to use for plugging a nd playing their data to derive business insights.
\n DTSTART:20191017T083000 SUMMARY:Unsupervised and Supervised Machine Learning on Big Graph – Case St udies DTEND:20191017T092959 LOCATION: See Description END:VEVENT END:VCALENDAR