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Graph Analytics has long demonstrated that it solves real-world problems including Fraud, Ranking, Recommendation, text summarization and other NLP tasks.
More recently, Graph Machine Learning applied directly on graphs using graph algorithms and machine learning, has been demonstrating significant advantages in solving the same problems as graph analytics as well as problems that are impractical to solve using graph analytics. Graph Machine Learning does this by training statistical models on the graph resulting in Graph Embeddings and Graph Neural Networks that are used to complex problems in a different way.
In this talk, we will compare and contrast these two approaches (spoiler: often complexity vs precision) in real-world scenarios. What factors should you consider when choosing one over the other and when do you even have a choice? Join this talk to learn about exciting new developments in Graph ML, as the graph techniques on which they are based.
Talk+Live Q&A at the Eastern Auditorium in Connected Data World Center; Free Live Streaming
You need an access pass to attend this session: Full Access Pass, Diversity Access Pass or Free Access Pass apply