Endüstri Mühendisliği Bölümü Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942
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Browsing Endüstri Mühendisliği Bölümü Koleksiyonu by Institution Author "Güney, Evren"
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Article Citation - WoS: 26Citation - Scopus: 26An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks(Elsevier, 2019) Güney, EvrenThe influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program that approximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently, we propose a linear programming relaxation based method with a provable worst case bound that converges to the current state-of-the-art 1-1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in terms of solution quality and this is one of the few studies that provides approximate optimal solutions for certain real life social networks.Article An Efficient Linear Programming Based Method for the Influence Maximization Problem in Social Networks (vol 503, Pg 589, 2019)(Elsevier, 2020) Güney, EvrenThe influence maximization problem (IMP) aims to determine the most influential individuals within a social network. In this study first we develop a binary integer program thatapproximates the original problem by Monte Carlo sampling. Next, to solve IMP efficiently,we propose a linear programming relaxation based method with a provable worst casebound that converges to the current state-of-the-art 1 − 1/e bound asymptotically. Experimental analysis indicate that the new method is superior to the state-of-the-art in termsof solution quality and this is one of the few studies that provides approximate optimalsolutions for certain real life social networks.Article Büyük Ölçekli Etki Enbüyükleme Problemi için Lagrange Gevşetmesi Tabanlı Etkin Bir Çözüm Yöntemi(AKÜ FEMÜBİD, 2020) Güney, EvrenEtki Enbüyükleme Problemi (EEP) büyük bir sosyal ağ içindeki en etkin K tane kişiyi seçen zor bir stokastik kombinatoryal eniyileme problemidir. Son yıllarda pek çok araştırmacının ilgisini çeken bu problem için çok sayıda etkin yöntem geliştirilmiştir. Sosyal ağdaki bilginin / etkinin yayılımı çeşitli ağ akış modelleri ile tasarlandığında, elde edilen problemin amaç fonksiyonunun alt-birimsel olduğu gözlemlenmiştir. Bu sebeple basit bir açgözlü algoritma ile (1-1/e) en kötü performans garantisine erişilmiştir. Ancak, aç gözlü algoritmanın büyük boyutlu problemlerde çok uzun çözüm süreleri gerektirmesi alternatif yöntem arayışlarına neden olmuştur. Son yıllarda geliştirilen yeni yöntemler genelde büyük boyutlu ağlarda kısa sürede iyi çözümler elde ederken (1-1/e) performans garantisini de korumaktadır. Ancak pek az sayıda çalışma problemin sadece en-iyi çözümüne odaklanmıştır. Bu çalışmada Lagrange gevşetmesi tabanlı ve EEP’yi eniyi / eniyiye yakın çözen ve ölçeklenebilen bir yöntem geliştirilmiştir. Bu çerçevede, öncelikle Örneklem Ortalama Yakınsaması ile özgün probleme yakınsayan belirgin bir matematiksel model kurulmuştur. Daha sonra bu model üzerinde düğüm tabanlı Lagrange gevşetmesi tekniği uygulanmıştır. İlgili yöntem bağımsız çağlayan ve doğrusal eşik bilgi yayılım modelleri varsayımı altında çeşitli boyutlardaki sosyal ağ veri setleri (Facebook, Enron, Gnutella, arXiv) üzerinde test edilmiştir. Bütün senaryolarda eniyi / eniyiye yakın çözümlere ulaşılırken yazındaki mevcut yöntemlere göre on kata kadar hızlanma sağlanmıştır.Article Citation - WoS: 33Citation - Scopus: 35Large-Scale Influence Maximization Via Maximal Covering Location(Elsevier, 2020) Güney, Evren; Ruthmair, Mario; Sinnl, Markus; Leitner, MarkusInfluence maximization aims at identifying a limited set of key individuals in a (social) network which spreads information based on some propagation model and maximizes the number of individuals reached. We show that influence maximization based on the probabilistic independent cascade model can be modeled as a stochastic maximal covering location problem. A reformulation based on Benders decomposition is proposed and a relation between obtained Benders optimality cuts and submodular cuts for correspondingly defined subsets is established. We introduce preprocessing tests, which allow us to remove variables from the model and develop efficient algorithms for the separation of Benders cuts. Both aspects are shown to be crucial ingredients of the developed branch-and-cut algorithm since real-life social network instances may be very large. In a computational study, the considered variants of this branch-and-cut algorithm outperform the state-of-the-art approach for influence maximization by orders of magnitude.Article Citation - WoS: 12Citation - Scopus: 18Minimizing the Misinformation Spread in Social Networks(Taylor and Francis, 2019) Güney, Evren; Kuban, İ. Kuban Altınel; Tanınmış, Kübra; Aras, Necati; Altinel, I. KubanThe Influence Maximization Problem has been widely studied in recent years, due to rich application areas including marketing. It involves finding k nodes to trigger a spread such that the expected number of influenced nodes is maximized. The problem we address in this study is an extension of the reverse influence maximization problem, i.e., misinformation minimization problem where two players make decisions sequentially in the form of a Stackelberg game. The first player aims to minimize the spread of misinformation whereas the second player aims its maximization. Two algorithms, one greedy heuristic and one matheuristic, are proposed for the first player’s problem. In both of them, the second player’s problem is approximated by Sample Average Approximation, a well-known method for solving two-stage stochastic programming problems, that is augmented with a state-of-the-art algorithm developed for the influence maximization problem.Article Citation - WoS: 2Citation - Scopus: 3Qubo Formulations and Characterization of Penalty Parameters for the Multi-Knapsack Problem(IEEE-Inst Electrical Electronics Engineers Inc, 2025) Guney, Evren; Ehrenthal, Joachim; Hanne, ThomasThe Multi-Knapsack Problem (MKP) is a fundamental challenge in operations research and combinatorial optimization. Quantum computing introduces new possibilities for solving MKP using Quadratic Unconstrained Binary Optimization (QUBO) models. However, a key challenge in QUBO formulations is the selection of penalty parameters, which directly influence solution feasibility and algorithm performance. In this work, we develop QUBO formulations for two MKP variants-the Multidimensional Knapsack Problem (MDKP) and the Multiple Knapsack Problem (MUKP)-and provide an algebraic characterization of their penalty parameters. We systematically evaluate their impact through quantum simulation experiments and compare the performance of the two leading quantum optimization approaches: Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing, alongside a state-of-the-art classical solver. Our results indicate that while classical solvers remain superior, careful tuning of penalty parameters has a strong impact on quantum optimization outcomes. QAOA is highly sensitive to parameter choices, whereas quantum annealing produces more stable results on small to mid-sized instances. Further, our results reveal that MDKP instances can maintain feasibility at penalty values below theoretical bounds, while MUKP instances show greater sensitivity to penalty reductions. Finally, we outline directions for future research in solving MKP, including adaptive penalty parameter tuning, hybrid quantum-classical approaches, and practical optimization strategies for QAOA, as well as real-hardware evaluations.
