Güney, Evren
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Güney, Evren
Guney, E.
Evren Güney
Guney, E.
Evren Güney
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guneye@mef.edu.tr
Main Affiliation
02.01. Department of Industrial Engineering
Status
Current Staff
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Documents
18
Citations
307
h-index
7
Documents
0
Citations
0
Scholarly Output
16
Articles
6
Views / Downloads
3528/5596
Supervised MSc Theses
8
Supervised PhD Theses
0
WoS Citation Count
69
Scopus Citation Count
78
WoS h-index
3
Scopus h-index
3
Patents
0
Projects
2
WoS Citations per Publication
4.31
Scopus Citations per Publication
4.88
Open Access Source
10
Supervised Theses
8
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| Journal | Count |
|---|---|
| Information Sciences | 2 |
| Computers & Operations Research | 1 |
| European Journal of Operational Research | 1 |
| Afyon Kocatepe Üniversitesi Fen ve Mühendislik Bilimleri Dergisi | 1 |
| Iise Transactions | 1 |
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Now showing 1 - 10 of 16
Article Benders Decomposition Algorithms for Minimizing the Spread of Harmful Contagions in Networks(Pergamon-elsevier Science Ltd, 2024) Sinnl, Markus; Tanınmış, Kübra; Güney, Evren; Aras, NecatiThe COVID-19 pandemic has been a recent example for the spread of a harmful contagion in large populations. Moreover, the spread of harmful contagions is not only restricted to an infectious disease, but is also relevant to computer viruses and malware in computer networks. Furthermore, the spread of fake news and propaganda in online social networks is also of major concern. In this study, we introduce the measure -based spread minimization problem (MBSMP), which can help policy makers in minimizing the spread of harmful contagions in large networks. We develop exact solution methods based on branch -and -Benders -cut algorithms that make use of the application of Benders decomposition method to two different mixed -integer programming formulations of the MBSMP: an arc -based formulation and a path -based formulation. We show that for both formulations the Benders optimality cuts can be generated using a combinatorial procedure rather than solving the dual subproblems using linear programming. Additional improvements such as using scenario -dependent extended seed sets, initial cuts, and a starting heuristic are also incorporated into our branch -and -Benderscut algorithms. We investigate the contribution of various components of the solution algorithms to the performance on the basis of computational results obtained on a set of instances derived from existing ones in the literature.Correction 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 Citation - WoS: 11Citation - Scopus: 18Minimizing the Misinformation Spread in Social Networks(Taylor and Francis, 2019) Güney, Evren; Kuban, İ. Kuban Altınel; Tanınmış, Kübra; Aras, NecatiThe 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.Master Thesis Vote Transtition Analysis and Comparison of Turkish Local Elections in 2014 and 2019(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Baydoğan, Ufuk; Güney, EvrenDebates around how voters switched their votes relative to previous elections are always the topic after the Election Day. Turkish local election of 2019 was important because of three reasons: first, because it was the first local election after Turkey adapted the new presidential system and the President also participated in the election campaign for his party; second, because İstanbul election, originally run on March 31, was ruled for rerun by Supreme Election Council and the third, because the electoral alliances had significant impact on the results where the votes for The People's Alliance significantly collapsed. This study presents a comparative analysis of 2014 and 2019 official Turkish Local Election Results as well as 2019 Re-Run Election Results of Istanbul to understand the vote transitions. As the outcomes are considered, there are significant changes in the distribution of voting rates between these elections, especially in critical metropolitans. Using the aggregate level vote counts, the vote transition probabilities between the elections are inferred using ecological inference. Proposed clustering approach on vote transition probabilities show that CHP and IYI Party have benefited from forming Nation’s Alliance for most of the cities mainly due to the vote switches from HDP and MHP. For the re-run election case, the slight number of vote difference between the alliances in March has increased significantly. This is mainly because of the contribution of absentees to Nation’s Alliance and around %5 of the People’s Alliance supporters in March who estimated to vote for Nation’s Alliance.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: 31Citation - Scopus: 33Large-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.Master Thesis Music Generation Using Deep Learning Techniques(MEF Üniversitesi Fen Bilimleri Enstitüsü, 2021) Akalın, Kutay; Evren GüneyThis project aims to generate songs using the Jukebox model and its architecture. Jukebox’s Vector Quantized Variational AutoEncoder (VQ-VAE) architecture is state-of-the-art deep generative model used for music generation and gives an outstanding result. For this purpose, different Elvis Presley songs were analyzed in audio domain using various Music Information Retrieval (MIR) methods. The top level of the Jukebox model was retrained with these songs in order to increase the quality of the songs that will be produced in the style of Elvis Presley. After that, 3 new samples were generated using the first six seconds of Elvis Presley - Jailhouse Rock as the input signal. At the end, these new songs were analyzed and compared.Article Citation - WoS: 1Citation - Scopus: 1Qubo 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.Master Thesis Retention Period Prediction for Pension Policies(MEF Üniversitesi, Fen Bilimleri Enstitüsü, 2019) Bayır, Ömer; Güney, EvrenCustomer Retention in Pension market refers to the activities and actions companies and organizations take to reduce the number of customer defections. How long the customer will be with our company or will stay in the system is retention. There are already workings in my company and other companies in the market about customer retention. Existing works generally contains how to measure customer retention and how to define distribution channels are successful in customer retention. Also existing predictive models are working on the feature set customer fund, total collection, un-paid premium frequency in general. In pension market companies have small margin of profit from pension policies. To make a profit from pension policies the companies have to retain their customer for long years. It ‘s approximately nine year to make profit from a pension policy because of high sales costs. Therefore to gain a new customer is less profitable than retaining present customers in Pension Market. In my project, I want to look retention in the pension application phase of customer. My main purpose is when the customer applied for pension product predict its retention period. If I produce an applicable model, It will be used in my company’s sales channels.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.
