Computer Engineering / Bilgisayar Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/10
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Conference Object A Novel Approach To Information Spreading Models for Social Networks(International Academy, Research, and Industry Association (IARIA), 2017) Sayın, Burcu; Şahin, SerapAnalyzing and modelling the spreading of any information through a social network (SN) is an important issue in social network analysis. Proposed solutions for this issue do not only help observing the information diffusion but also serve as a valuable resource for predicting the characteristics of the network, developing network-specific advertising etc. Up-to-date approaches include probabilistic analysis of information spreading and the information cascade models. In this paper, we propose a hybrid model which considers an information spreading model, combines it with cascades and social behavior analysis. We propose a new hybrid usage approach to represent a real-world modelling for information spreading process.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.