GROUND TRUTH AND METADATA RELATIONSHIP IN SBM COMMUNITY DETECTION: SCHOOL FRIENDSHIP NETWORK

Abstract

Many data sets which are studied by Information Systems researchers involve networks that exhibits community structure. Dividing the large networks into manageable groups (communities) is a crucial first step to understand the network in macro scale. Which then enables the researchers to analyze the data in meso-scale. In our previous work we presented Stochastic Block Model approach and compared the metadata with the ground truth. In present study we introduce a statistical technique called neoSBM that can reveal the relationship between metadata and the community structure on the same real-world school best friendship data set.

Keywords

• Community Detection,SBM,neoSBM,,Best Friends Network

SBM TOPLULUK TESPİTİNDE TEMEL GERÇEK VE ÜST VERİ İLİŞKİSİ: OKUL ARKADAŞLIK AĞI

Abstract

Yönetim Bilişim Sistemleri araştırmacılarının ilgi alanına giren ağların birçoğunda topluluk yapısına rastlanır. Bu makro ölçekli yapılarda doğal olarak ortaya çıkan toplulukların tespit edilmesi büyük veri kümelerinin yönetilebilir gruplara ayrılması açısından gereklidir. Böylece bu sistemlerin orta ölçekte anlaşılabilir hale gelmesi mümkün olur. Önceki çalışmamızda, Stokastik Blok Modelleme yaklaşımını kullanarak üst veri ve temel gerçeği karşılaştırdık. Bu çalışmamızda, üst veri ile topluluk yapısının ilişkisini ölçebilen bir istatistiksel yöntem olan neoSBM’i bir gerçek dünya arkadaşlık ağı veri seti üzerinde uygulayarak sunuyoruz.

Keywords

• Stokastik Blok Modelleme,Topluluk Tespiti,,Yakın Arkadaşlık Ağı

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