Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object A Comparative Analysis of Two Most Recent Dynamic Community Tracking Methods(01. Izmir Institute of Technology, 2019) Karataş, Arzum; Şahin, SerapReal world networks are intrinsically dynamic, and they are mostly represented by dynamic graphs in virtual world. Analysis of these dynamic network data can give valuable information for decision support systems in many domains in criminology, politics, health, advertising and social networks etc. Community tracking is important to analyze and understand the dynamics of the group structures and predict the near futures of communities. With a successful analysis of these data, software engineering tools and decision support systems can produce more successful results for end users. In this study, we present a comparative study of two important and recent community tracking methods in terms of accuracy, algorithmic complexity and their characteristics. We use a benchmark dataset which have ground truth community information detected each time step as a test bed.Conference Object Citation - Scopus: 2A Comparative Study of Modularity-Based Community Detection Methods for Online Social Networks(CEUR Workshop Proceedings, 2018) Karataş, Arzum; Şahin, SerapDigital data represent our daily activities and tendencies. One of its main source is Online Social Networks (OSN) such as Facebook, YouTube etc. OSN are generating continuously high volume of data and define a dynamic virtual environment. This environment is mostly represented by graphs. Analysis of OSN data (i.e.,extracting any kind of relations and tendencies) defines valuable information for economic, socio-cultural and politic decisions. Community detection is important to analyze and understand underlying structure and tendencies of OSNs. When this information can be analysed successfully, software engineering tools and decision support systems can produce more successful results for end users. In this study, we present a survey of selected outstanding modularity-based static community detection algorithms and do comparative analysis among them in terms of modularity, running time and accuracy. We use different real-world OSN test beds selected from SNAP dataset collection such as Facebook Ego network, Facebook Pages network (Facebook gemsec), LiveJournal, Orkut and YouTube networks.