Benzerlik Ölçülerine Dayalı Entropi ile Hipergraflarda Merkezilik Tespiti

Year 2023, Volume: 14 Issue: 3, 407 - 419, 30.09.2023

İhsan Tuğal , Zeydin Pala

https://doi.org/10.24012/dumf.1241450

Abstract

Hipergarflar ve basit kompleksler, daha yüksek dereceli etkileşimleri modellemek için kullanılabilir. Graflar ikili etkileşimleri modellemek ve açıklamakla sınırlıdır. Bu çalışmada hipergraflarda merkezilik konusu incelendi. Bu çalışmada hipergraflardaki düğümlerin ve hiper kenarların entropisine dayalı yeni merkezilik ölçüm yöntemleri önerildi. Şimdiye kadar, merkezi düğümleri belirlemek için çeşitli perspektiflerden birçok hesaplama önerilmiştir, ancak hiçbiri merkezilik sorununa tam bir çözüm sağlamamaktadır. Çünkü merkeziliğe farklı bakış açıları var. Merkezilik problemlerinde çözüme ulaşmak için farklı modellerin denenmesi önemlidir. Belirsizliğin bir ölçüsü olan entropi, merkezilik ölçümlerinde yol göstericidir. Merkezilik içim ideal çözümler üretebilir. Karmaşık sistemlerde entropi farklı yöntemlerle ölçülebilir. Bu çalışmada düğümler için birleşim, kesişim ve jaccard benzerlik değerlerine göre entropi hesabı yapıldı. Benzerliğin ölçülme şekli, merkeziliğin türünü gösterir. Derece ve birleşim benzerlik değerleri kullanıldığında yerel merkezilikler tespit edildi. Kesişim ve jaccard benzerlikleri bize global merkezilikleri gösterdi. Geleneksel merkezilik yöntemleri de önerilen yöntemin sonuçlarıyla karşılaştırıldı. Yöntemin doğruluğu farklı hipergraf veri setleri ile test edildi. Hipergraflar da isteklerimize göre farklı benzerlik parametreleri ile verimli sonuçlar üretilebileceği gösterildi.

Keywords

merkezilik, entropi, hipergraf

Centrality with Entropy in Hypergraphs Based on Similarity Measures

Abstract

Hypergraphs and simplicial complexes can be used to model higher-order interactions. Graphs are limited to model and describe pairwise interactions. In this study, the issue of centrality in hypergraphs was studied. We introduce centrality measures based on the entropy of nodes and hyperedges in the hypergraphs. Until now, a lot of measures from various perspectives have been proposed to identify influential nodes, yet non provides a complete solution to the centrality problem. Because there are different perspectives on centrality. It is important to try different models to reach a solution in centrality problems. Entropy, which is a measure of uncertainty, is a guide in centrality measurements. It can produce ideal solutions for centrality. In complex systems, the entropy can be measured by different methods. In this study, the entropy calculation was made according to the union, intersection, and jaccard similarity values for nodes. The way that similarity is measured indicates the type of centrality. Local centralities were detected more precisely when the degree and union similarity values were used. The intersection and jaccard similarities showed us the global centralities. Traditional methods of centrality were also compared with the results of the proposed method. The accuracy of the method was tested with different hypergraph datasets. It has been shown that we can produce efficient results with different similarity parameters according to our wishes in hypergraphs.

Keywords

centrality, entropy, hypergraph

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