Sosyal ağların çizge entropi kullanılarak analiz edilmesi ve uygulamaları
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Dosyalar
Tarih
2019
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Kompleks yapılarda ölçüt sağlayan kavramlardan biri entropidir. Bu çalışmada çizge entropi kullanılarak sosyal ağlarda analizler yapıldı ve uygulamaları gösterildi. Sosyal ağların önemli problemlerinden olan merkezilik hesaplamaları için yeni yöntemler önerildi. Entropinin ağ düğümlerinin merkeziliklerinin tespitindeki yeteneği gösterildi. Entropi hesaplamaları Karcı entropi, Renyi entropi ve Shannon entropi ile yapıldı. Daha önce sosyal ağlarda hiç kullanılmamış Karcı entropi sosyal ağlara uygulanmış oldu. Shannon ve Renyi entropi ile kıyaslandı. Karcı entropi ve Renyi entropide kullanılan 𝛼���� değeri için ağın topolojik özelliklerinden olan yoğunluk ve kümelenme katsayısı kullanılarak bulanık 𝛼���� seçim algoritması önerildi. Önerilen yöntemler Flags, Air Traffic ve Netscience veri setlerine uygulandı. Analiz sonuçları geleneksel merkezilik ölçümleri olan derece, arasındalık, yakınlık ve özvektör merkeziliği ile kıyaslandı. Önerilen yöntemin doğruluğu, etkinliği ve uygulanabilirliği gösterildi. Yerel ve küresel ölçümler yapılabildi. Geleneksel yöntemlerin çözüm üretemediği bazı karmaşık yapılarda Karcı entropi ve Renyi entropi ile en etkin aktörler tespit edilebildi. Sosyal ağlarda düğüm derecelerinin ve kenar ağırlıklarının etkisi beraber ölçülebildi. Sosyal ağlara yeni veri setleri kazandırıldı.
One of the concepts that provide criteria in complex structures is entropy. In this study, graph entropy was used to analyze social networks and their applications were shown. New methods were proposed for node centrality, one of the major problems of social networks. The ability of entropy to determine the centrality of network nodes was demonstrated. Entropy calculations were performed with Karcı entropy, Renyi entropy and Shannon entropy. Karcı entropy, which had never been used in social networks before, was applied to the social networks. A fuzzy α selection algorithm was proposed to determine the α value used in the Karcı entropy and Renyi entropy using density and clustering coefficient, which are the topological properties of the network. The proposed methods were applied to the Flags, Air Traffic, and Netscience data sets. Karcı entropy was compared with Renyi and Shannon entropies. The results of the analysis were compared with the traditional centrality measures which are degree, betweenness, closeness, and eigenvector centralities. The accuracy, effectiveness, and applicability of the proposed method were shown. Local and global measurements were performed. Karcı entropy and Renyi entropy were able to identify the influential actors in some complex systems where conventional methods cannot find a solution. The effect of node degrees and edge weights to the centrality could be measured together. New data sets were introduced to social networks.
One of the concepts that provide criteria in complex structures is entropy. In this study, graph entropy was used to analyze social networks and their applications were shown. New methods were proposed for node centrality, one of the major problems of social networks. The ability of entropy to determine the centrality of network nodes was demonstrated. Entropy calculations were performed with Karcı entropy, Renyi entropy and Shannon entropy. Karcı entropy, which had never been used in social networks before, was applied to the social networks. A fuzzy α selection algorithm was proposed to determine the α value used in the Karcı entropy and Renyi entropy using density and clustering coefficient, which are the topological properties of the network. The proposed methods were applied to the Flags, Air Traffic, and Netscience data sets. Karcı entropy was compared with Renyi and Shannon entropies. The results of the analysis were compared with the traditional centrality measures which are degree, betweenness, closeness, and eigenvector centralities. The accuracy, effectiveness, and applicability of the proposed method were shown. Local and global measurements were performed. Karcı entropy and Renyi entropy were able to identify the influential actors in some complex systems where conventional methods cannot find a solution. The effect of node degrees and edge weights to the centrality could be measured together. New data sets were introduced to social networks.
Açıklama
Anahtar Kelimeler
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
Kaynak
WoS Q Değeri
Scopus Q Değeri
Cilt
Sayı
Künye
Tuğal, İ. (2019). Sosyal ağların çizge entropi kullanılarak analiz edilmesi ve uygulamaları. Yayınlanmış Doktora Tezi, İnönü Üniversitesi.
