BigDataX Presents: GraphAI – Machine Learning on Network Data

BigDataX Presents: GraphAI – Machine Learning on Network Data

Drinks will be provided and sponsorsed by Spencer Ogden!

== Description ==
Machine learning algorithms are typically applied on tabular data, where rows are the labelled or unlabelled observations, and columns are the features and the label(s). Graph data bases are different from tabular formats. During this session, we will use LynxKite – a big graph analytics tool – for creating AI models to better understand the structure of a social network and create predictions for unknown attributes.

== About the Speaker ==
Gábor Benedek is an innovation partner at Lynx Analytics providing predictive analytics for communication companies, financial services and healthcare sectors in South-East-Asia. He has been applying SNA methodologies for Celcom, Indosat, Singtel, Telkomsel, Globe, DBS Bank in the region. Gábor received his PhD in 2003, in 2012 his T-Mobile SNA churn study was awarded as the Best Application paper by the European Decision Science Institute, and he is the author of one book and author/coauthor of over 20 articles. He was an Associate Professor at Corvinus University of Budapest, researching and lecturing in the fields of economic and business simulations, social network analysis, econometrics, data mining and predictive analytics. Gábor was among the founders of Data Explorer, the first consulting company in predictive analytics in Hungary. In 1999 Data Explorer built the first social network analysis software for churn and community detection applicable for mobile customers in Europe, based on Gábor’s theoretical foundations and proposals. In 2010 Gabor was contributing to the largest public physicians’ social network in the world, based on real patient-flow data between general practitioners and specialists.

== Date & Location ==
29th November, Thursday 7pm – 9pm
One Raffles Place, Office Tower 2, #34-61, Singapore 048616

== Target audience ==
DS and DE

== Prerequisites ==
Basic statistics (linear regression), affinity for coding
Laptops, google chrome needed