İşaretli Sosyal Ağlarda Etki Maksimizasyonu İçin Yeni Bir Aç Gözlü Algoritma

Sosyal etki insanların görüşlerini şekillendiren büyük olgulardan biridir. Bu bakımdan, Etki Maksimizasyonu (EM) problemi viral pazarlama, kamuoyu şekillendirme gibi pratik faydaları olduğu için sosyal ağ analizinde en fazla ilgili çeken araştırma alanlarından biridir. EM probleminin amacı bir sosyal ağ üzerindeki etkili kişi olarak adlandırılan az sayıdaki kişiyi kullanarak bir etkinin (bir fikir veya reklam) ağ üzerindeki yayılımını maksimize etmektir. Etkili kişilerin tespiti birçok durumda NP-Hard bir kombinasyonal optimizasyon problemidir. Bundan dolayı, EM problemi için birçok algoritma geliştirilmiştir ve geliştirilmeye devam etmektedir. Ne var ki, geliştirilen algoritmalar henüz çözüm kalitesi ve hız açısından istenen seviyede değildirler. Bu çalışmada, bireyler arasındaki olumlu ve olumsuz ilişkileri göz önünde bulunduran işaretli EM problemine odaklanılmıştır. Bu amaçla, en iyi adet etkili kişiyi tespit etmek için Elitist Aç Gözlü Algoritma (EGA) olarak adlandırılan bir aç gözlü algoritma geliştirmiştir. EGA’nın performansı 2 adet açık veriseti üzerinde rasgele seçim, çıkış derecesi merkeziliği, ve bir güncel algoritma ile kıyaslanmıştır. EGA çözüm kalitesi açısından rakiplerine göre daha iyi sonuçlar vermiştir.

Anahtar Kelimeler:

Etki maksimizasyonu, online sosyal ağlar, bilgi yayılımı, aç gözlü algoritma

A New Greedy Algorithm For Influence Maximization On Signed Social Networks

The social influence is one of the major phenomenons that shapes people's decisions. In this respect, Influence Maximization (IM) problem is one of the most attractive research topics in the social network analysis because its practical benefits in viral marketing, public opinions shaping etc. The IM problem aims to maximize the spread of an influence (e.g. an opinion, an advertisement) in a social network by using a small number of the most effective individuals, whom is called influencers. Detecting the influencers is the NP-Hard combinatorial optimization problem in most cases. Therefore, many algorithms have been and are being developed for the IM problem. However, the algorithms have not yet achieved to the desired solution quality and speed. In this study, we focused on the signed IM problem that is considers both positive and negative influence between the individuals. For this purpose, we developed a greedy algorithm called the Elitist Greedy Algorithm (EGA) for detecting influencers set. We compared the EGA’s performance on 2 public datasets with random seed selection, out degree heuristic, and one state-of-the-art greedy algorithm. The EGA outperforms the competitors in terms of the achieved total influence.

Keywords:

Influence maximization, online social networks, information diffusion, greedy algorithm,

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