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Title: NetDriller Version 2: A Powerful Social Network Analysis Tool
Authors: Afra, Salim
Özyer, Tansel
Rokne, Jon
Alhajj, Reda
Keywords: Social network analysis
data Mining
network construction
link prediction
hierarchical zooming
Issue Date: 2018
Publisher: IEEE Computer Society
Source: Afra, S., Özyer, T., and Rokne, J. (2018, November). NetDriller Version 2: A Powerful Social Network Analysis Tool. In 2018 IEEE International Conference on Data Mining Workshops (ICDMW) (pp. 1475-1480). IEEE.
Abstract: Social network analysis has gained considerable attention since Web 2.0 emerged and provided the ground for two-ways interaction platforms. The immediate outcome is the availability of raw datasets which reflect social interactions between various entities. Indeed, social networking platforms and other communication devices are producing huge amounts of data which form valuable sources for knowledge discovery. Hence the need for automated tools like NetDriller capable of successfully maximizing the benefit from networked data. Most datasets which reflect kind of many to many relationship can be represented as a network which is a graph consisting of actors having relationships among each other. Many tools exist for network analysis inspired to extract knowledge from a constructed network. However, most of these tools require users to prepare as input a dataset that inspires the complete network which is then displayed and analyzed by the tool using the measures supported. A different perspective has been employed to develop NetDriller as a network construction and analysis tool which does some tasks beyond what is normally available in existing tools. NetDriller covers the lack that exists in other tools by constructing a network from raw data using data mining techniques. In this paper, we describe the second version of NetDriller which has been recently improved by adding new functions for a richer and more effective network construction and analysis. This keeps the tool up to date and with high potential to handle the huge volume of networks and the different types of raw data available for analysis.
Description: 18th IEEE International Conference on Data Mining Workshops ( 2018: Singapore; Singapore )
ISBN: 9.78154E+12
ISSN: 2375-9232
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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