Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2652
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dc.contributor.authorAfra, Salim-
dc.contributor.authorÖzyer, Tansel-
dc.contributor.authorRokne, Jon-
dc.contributor.authorAlhajj, Reda-
dc.date.accessioned2019-12-25T14:01:59Z
dc.date.available2019-12-25T14:01:59Z
dc.date.issued2018
dc.identifier.citationAfra, 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.en_US
dc.identifier.isbn9.78154E+12
dc.identifier.issn2375-9232
dc.identifier.urihttps://ieeexplore.ieee.org/document/8637472-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/2652-
dc.description18th IEEE International Conference on Data Mining Workshops ( 2018: Singapore; Singapore )
dc.description.abstractSocial 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.en_US
dc.language.isoenen_US
dc.publisherIEEE Computer Societyen_US
dc.relation.ispartof2018 IEEE International Conference on Data Mining Workshops (ICDMW)en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSocial network analysisen_US
dc.subjectdata Miningen_US
dc.subjectnetwork constructionen_US
dc.subjectlink predictionen_US
dc.subjecthierarchical zoomingen_US
dc.titleNetdriller Version 2: a Powerful Social Network Analysis Toolen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage1475
dc.identifier.endpage1480
dc.identifier.wosWOS:000465766800202en_US
dc.identifier.scopus2-s2.0-85062846888en_US
dc.institutionauthorÖzyer, Tansel-
dc.identifier.doi10.1109/ICDMW.2018.00211-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusquality--
item.openairetypeConference Object-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
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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