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, Serap
    Real 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.