Influence maximization in social networks under Deterministic Linear Threshold Model

Name: Influence maximization in social networks under Deterministic Linear Threshold Model
Author: Gürsoy, F., Danış, Dilek Günneç

İsim	Influence maximization in social networks under Deterministic Linear Threshold Model
Yazar	Gürsoy, F., Danış, Dilek Günneç
Basım Tarihi:	2018-12
Basım Yeri	- Elsevier
Konu	Influence maximization, Social networks, Diffusion models, Targeted marketing, Greedy algorithm
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0950-7051
Kayıt Numarası	27c7d9dd-6fac-4f4f-a95b-3170916b66a1
Lokasyon	Industrial Engineering
Tarih	2018-12
Notlar	TÜBİTAK
Örnek Metin	We define the new Targeted and Budgeted Influence Maximization under Deterministic Linear Threshold Model problem and develop the novel and scalable TArgeted and BUdgeted Potential Greedy (TABU-PG) algorithm which allows for optional methods to solve this problem. It is an iterative and greedy algorithm that relies on investing in potential future gains when choosing seed nodes. We suggest new real-world mimicking techniques for generating influence weights, thresholds, profits, and costs. Extensive computational experiments on four real network (Epinions, Academia, Pokec and Inploid) show that our proposed heuristics perform significantly better than benchmarks. We equip TABU-PG with novel scalability methods which reduce runtime by limiting the seed node candidate pool, or by selecting more nodes at once, trading-off with spread performance.
DOI	10.1016/j.knosys.2018.07.040
Cilt	161

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Influence maximization in social networks under Deterministic Linear Threshold Model

Yazar Gürsoy, F., Danış, Dilek Günneç

Basım Tarihi 2018-12

Basım Yeri - Elsevier

Konu Influence maximization, Social networks, Diffusion models, Targeted marketing, Greedy algorithm

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0950-7051

Kayıt Numarası 27c7d9dd-6fac-4f4f-a95b-3170916b66a1

Lokasyon Industrial Engineering

Tarih 2018-12

Notlar TÜBİTAK

Örnek Metin We define the new Targeted and Budgeted Influence Maximization under Deterministic Linear Threshold Model problem and develop the novel and scalable TArgeted and BUdgeted Potential Greedy (TABU-PG) algorithm which allows for optional methods to solve this problem. It is an iterative and greedy algorithm that relies on investing in potential future gains when choosing seed nodes. We suggest new real-world mimicking techniques for generating influence weights, thresholds, profits, and costs. Extensive computational experiments on four real network (Epinions, Academia, Pokec and Inploid) show that our proposed heuristics perform significantly better than benchmarks. We equip TABU-PG with novel scalability methods which reduce runtime by limiting the seed node candidate pool, or by selecting more nodes at once, trading-off with spread performance.

DOI 10.1016/j.knosys.2018.07.040

Cilt 161