Dynamic modularity based community detection for large scale networks

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2015

Öğrenci: RIZA AKTUNÇ

Danışman: İSMAİL HAKKI TOROSLU

Özet:

In this work, a new fast dynamic community detection framework for large scale networks is presented. Most of the previous community detection algorithms are designed for static networks. Static modularity optimizer framework (SMO), which is introduced by Waltman & Van Eck, consists of such community detection algorithms. However, large scale social networks are dynamic and evolve frequently over time. To quickly detect communities in dynamic large scale networks, we proposed dynamic modularity optimizer framework (DMO) that is constructed by making the modularity based community detection algorithms placed in SMO dynamic. The proposed framework is tested on the mobile communication networks which are extracted from the raw call detail records (CDR) data of a GSM operator in Turkey. According to the results, community detection algorithms in the proposed framework perform better than algorithms in SMO when large scale dynamic networks are considered.