2018 Keynote Speaker

 "Complex network in big data problems"

Abstract:

Systems as diverse as brain or genetic networks are best described as networks with complex topology. A common topological property of many large networks is that the network connectivity generated by a few influential nodes follow a scale-free distribution. The scale-free (power-law) distribution is given by the probability that one vertex in the network interacts with  other vertices decays as a power law, following

 

It has been well understood that two important general components, growth and preferential attachment, generate such scale-free distribution. The growth is defined as the increase of the number of nodes of the network over time and the preferential attachment as assigning the probability  in a way that at a given time a new vertex connects vertex  proportional to the already given connectivity  of that vertex. In this talk, we discuss scale-free network and the two components in statistical point of view and then propose test statistics which enable to carry out formal statistical test against scale-free network.

We also discuss scale-free distribution in various types of big data problems, which includes social network, computer network, economic crisis, motor learning, language learning, city heavy rain, genetics and brain network.

 08-883

Tae Yoon Kim

Professor 

 Department of Statistics, Keimyung University

Daegu. Korea

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IMPORTANT DATES

Abstract/ Full Paper Submission Deadline

January 10, 2019

Notification of Acceptance 
From January 30, 2019

Early Bird Registration & Payment Deadline 

February 20, 2019

Regular Registration & Payment Deadline 

March 10, 2019

Conference Date 
May 7-9, 2019