Abstract
Sosyal etki insanlarin goruslerini sekillendiren buyuk olgulardan biridir. Bu bakimdan, Etki Maksimizasyonu (EM) problemi viral pazarlama, kamuoyu sekillendirme gibi pratik faydalari oldugu icin sosyal ag analizinde en fazla ilgili ceken arastirma alanlarindan biridir. EM probleminin amaci bir sosyal ag uzerindeki etkili kisi olarak adlandirilan az sayidaki kisiyi kullanarak bir etkinin (bir fikir veya reklam) ag uzerindeki yayilimini maksimize etmektir. Etkili kisilerin tespiti bircok durumda NP-Hard bir kombinasyonal optimizasyon problemidir. Bundan dolayi, EM problemi icin bircok algoritma gelistirilmistir ve gelistirilmeye devam etmektedir. Ne var ki, gelistirilen algoritmalar henuz cozum kalitesi ve hiz acisindan istenen seviyede degildirler. Bu calismada, bireyler arasindaki olumlu ve olumsuz iliskileri goz onunde bulunduran isaretli EM problemine odaklanilmistir. Bu amacla, en iyi adet etkili kisiyi tespit etmek icin Elitist Ac Gozlu Algoritma (EGA) olarak adlandirilan bir ac gozlu algoritma gelistirmistir. EGA’nin performansi 2 adet acik veriseti uzerinde rasgele secim, cikis derecesi merkeziligi, ve bir guncel algoritma ile kiyaslanmistir. EGA cozum kalitesi acisindan rakiplerine gore daha iyi sonuclar vermistir.
Highlights
Online Social Networks (OSNs) are the digital world equivalent of real social networks
In a more formal description, the Influence Maximization (IM) problem is the detection of top-k influencers that will maximize the propagation of a desired influence on a social network modeled as Graph G under a particular propagation model [1], [2]
The IM problem has been formulated as a combinatorial optimization problem, and its complexity is NP-Hard under many propagation models such as Independent Cascade (IC) model and Linear Threshold (LT) model [2]–[4]
Summary
First choose top n nodes in the scope of influence spread. pick the remaining nodes by their influence capabilities. Li et al have developed an algorithm for the IM problem using community detecting approaches [17] It firstly partitions the network into n communities, and it picks the most central nodes in each community as a seed. Li et al, have dealt with signed influence maximization problem, and suggested a more appropriate propagation model called Polarity-related Independent Cascade (IC-P) [6] They have developed a greedy algorithm called the IC-P Greedy. Heuristic-greedy algorithms use centrality measures as proxies (heuristics) to estimate the nodes’ influence capabilities. They are much faster that the first category; their solution quality are very sensitive to the measure and the network structure
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