The natural phenomenon of social network formation has been studied by researchers in anthropology and sociology starting from early in the 20th century. The social network methodology was dependent on manual processes and hence its applications were limited to small networks involving mostly humans. The main theme was to analyze human interactions in specific environments in order to discover key persons, groups, etc. However, recent development in information technology and the wide availability of the World Wide Web has highly influenced and shaped the research in social networks. People are joining online social networks and socialize on the web, and there is a clear shift from real to virtual life. Today, social network research is much more multidisciplinary, requiring expertise in anthropology, sociology, behavioral science, psychology, statistics, mathematics, computer science, etc.

  The applications of the social network methodology are rapidly increasing. It is even hard to find a domain where the social network methodology is not applicable. Diverse sources of data are available for network construction. Here, we realized the need for a tool that integrates data mining and machine learning techniques in the model construction as well as in the analysis. Current tools mostly neglect the former and concentrate on the latter. Therefore, we initiated this project under the supervision of Dr. Reda Alhajj at University of Calgary to fill the gap in the social network model construction process by developing NetDriller, which is a tool for Social Network Analysis. NetDriller helps the analysts to create a social network or open a predefined network and perform some analysis and measurements on the network. Using the current version of NetDriller, various analyses can be performed on the loaded network, such as: measuring node and network level metrics, filtering links, finding bridges and cliques and shortest paths, folding 2-mode network and create 1-mode network, inverting the network, clustering nodes of the network, searching the nodes based on available node level metrics (containing fuzzy search). The source code of two open source projects (Jung and Weka) have been used in some parts of NetDriller.


   Dr. Reda Alhajj, Dr. Jon Rokne


   Keivan Kianmehr, Negar Koochakzadeh, Atieh Sarraf


   Alen Chia-Lung Chen (2010), Alex Chan (2010), Ali Rahmani (2010), Anthony Tam (2010), Atieh Sarraf (2010), Faisal Iqbal (2010), Fatemeh Keshavarz (2010), Ian Reinhart (2009), Jun Jiang (2010), Keivan Kianmehr (2009-present) Mohamed Nagi (2009-present), Mona Okasha (2009), Negar Koochakzadeh (2009-present), Omair Shafiq (2009), Sara Aghakhani (2010), Sonja Ridic (2010), Terence Ran Tang (2010)

Related papers

  Related papers are listed under the website of Dr. Reda Alhajj


   The copyright and proper citation is required if the tool is to be used.