2018 Keynote Speaker

 "Complex network in big data problems"


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.


Tae Yoon Kim


 Department of Statistics, Keimyung University

Daegu. Korea

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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