Big data refers to the data set that management, control and process of them are beyond the capabilities of software tools in the terms of both tolerated and expected time. Large scale data is also continuously growing in a single data set. During the expansion of the data set, greater depth of information is consistently found. This expansion is important due to offering valuable insights for data analyses. Social networks are one of the most popular examples of big data set.

Due to the increasing popularity of social networks, many researchers have been interested in the analysis of these networks. Link prediction is one of the research fields in social network analysis. We aim to solve the problem of link prediction for big data sets, by assuming the information of each node's neighbors is located near the node. The main purpose of our project is to propose a link prediction algorithm for big data which must run in scalable time, meaning that the running time of our algorithm does not increase exponentially.

Provider

هستی اکبری email: hasti.akbari@ut.ac.ir

 

Supervisor

مسعود اسدپور email: asadpour [AT] ut.ac.ir