Person: DANIŞ, Dilek Günneç
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Dilek Günneç
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DANIŞ
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ArticlePublication Metadata only Influence maximization in social networks under Deterministic Linear Threshold Model(Elsevier, 2018-12) Gürsoy, F.; Danış, Dilek Günneç; Industrial Engineering; DANIŞ, Dilek GünneçWe 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.ArticlePublication Metadata only A branch‐and‐cut approach for the least cost influence problem on social networks(Wiley, 2020-07) Danış, Dilek Günneç; Raghavan, S.; Zhang, R.; Industrial Engineering; DANIŞ, Dilek GünneçThis paper studies a problem in the online targeted marketing setting called the least cost influence problem (LCIP) that is known to be NP-hard. The goal is to find the minimum total amount of inducements (individuals to target and associated tailored incentives) required to influence a given population. We develop a branch-and-cut approach to solve this LCIP on arbitrary graphs. We build upon Gunnec et al.'s novel totally unimodular (TU) formulation for the LCIP on trees. The key observation in applying this TU formulation to arbitrary graphs is to enforce an exponential set of inequalities that ensure the influence propagation network is acyclic. We also design several enhancements to the branch-and-cut procedure that improve its performance. We provide a large set of computational experiments on real-world graphs with up to 155 000 nodes and 327 000 edges that demonstrates the efficacy of the branch-and-cut approach. This branch-and-cut approach finds solutions that are on average 1.87% away from optimality based on a test-bed of 160 real-world graph instances. We also develop a heuristic that prioritizes nodes that receive low influence from their peers. This heuristic works particularly well on arbitrary graphs, providing solutions that are on average 1.99% away from optimality. Finally, we observe that partial incentives can result in significant cost savings, over 55% on average, compared to the setting where partial incentives are not allowed.ArticlePublication Metadata only Least-cost influence maximization on social networks(Informs, 2020-03) Danış, Dilek Günneç; Raghavan, S.; Zhang, R.; Industrial Engineering; DANIŞ, Dilek GünneçViral-marketing strategies are of significant interest in the online economy. Roughly, in these problems, one seeks to identify which individuals to strategically target in a social network so that a given proportion of the network is influenced at minimum cost. Earlier literature has focused primarily on problems where a fixed inducement is provided to those targeted. In contrast, resembling the practical viral-marketing setting, we consider this problem where one is allowed to "partially influence" (by the use of monetary inducements) those selected for targeting. We thus focus on the "least-cost influence problem (LCIP)": an influence-maximization problem where the goal is to find the minimum total amount of inducements (individuals to target and associated tailored incentive) required to influence a given proportion of the population. Motivated by the desire to develop a better understanding of fundamental problems in social-network analytics, we seek to develop (exact) optimization approaches for the LCIP. Our paper makes several contributions, including (i) showing that the problem is NP-complete in general as well as under a wide variety of special conditions; (ii) providing an influence greedy algorithm to solve the problem polynomially on trees, where we require 100% adoption and all neighbors exert equal influence on a node; and (iii) a totally unimodular formulation for this tree case.ArticlePublication Metadata only Polarity classification of twitter messages using audio processing(Elsevier, 2020-11) Duşçu, Mihail; Danış, Dilek Günneç; Industrial Engineering; DANIŞ, Dilek GünneçPolarity classification is one of the most fundamental problems in sentiment analysis. In this paper, we propose a novel method, Sound Cosine Similaritye Matching, for polarity classification of Twitter messages which incorporates features based on audio data rather than on grammar or other text properties, i.e., eliminates the dependency on external dictionaries. It is useful especially for correctly identifying misspelled or shortened words that are frequently encountered in text from online social media. Method performance is evaluated in two levels: i) capture rate of the misspelled and shortened words, ii) classification performance of the feature set. Our results show that classification accuracy is improved, compared to two other models in the literature, when the proposed features are used.ArticlePublication Open Access Etki enbüyükleme problemi için ajan-bazlı modelleme yaklaşımı(Afyon Kocatepe Üniversitesi, 2018) Danış, Dilek Günneç; Industrial Engineering; DANIŞ, Dilek GünneçPazara yeni girecek bir ürünün öncelikliolarak kullanımına sunulacağı kişilerin (hedef kümenin) belirlenmesi pazar payı tahmini yapmak için önemli, ancak çözülmesi zor bir problemdir. Bu makalede, bu problem için ajan-bazlı modelleme ile bir simülasyonçalışması geliştirilmiştir. Hedef kümeye seçilmiş kişilerin sosyal ağ üzerindeki önemi, ikna becerileri, diğerlerinin yeni ürün adaptasyonugibi karakteristik özelliklerinve hedef küme büyüklüğününürünün yayılması üzerindeki etkileri incelenmektedir.Bu özelliklerebağlı 12 farklı senaryoiçinçözümler değerlendirilmiştir.ArticlePublication Metadata only Fair-fixture: minimizing carry-over effects in football leagues(American Institute of Mathematical Sciences, 2019-10) Danış, Dilek Günneç; Demir, E.; Industrial Engineering; DANIŞ, Dilek GünneçWe study a sports scheduling problem with the objective of minimizing carry-over effects in round robin tournaments. In the first part, focusing on tournaments that allow minimum number of breaks (at most one) for each team, we formulate an integer programming model and provide an efficient heuristic algorithm to solve this computationally expensive problem. We apply the algorithm to the current Turkish Professional Football League and present an alternative scheduling template. In the second part, we discuss how the carry-over effects can be further decreased if the number of breaks is allowed to be of slightly larger value and numerically represent this trade-off.ArticlePublication Metadata only Relief aid provision to en route refugees: Multi-period mobile facility location with mobile demand(Elsevier, 2022-09-01) Bayraktar, O. B.; Danış, Dilek Günneç; Salman, F. S.; Yücel, E.; Industrial Engineering; DANIŞ, Dilek GünneçMany humanitarian organizations aid en route refugee groups who are on their journey to cross borders using mobile facilities and need to decide the number and routes of the facilities. We define a multi-period facility location problem in which both the facilities and demand are mobile on a network. Refugee groups may enter and exit the network in different periods and follow various paths. In each period, a refugee group moves from one node to an adjacent one in their predetermined path. Each facility should be located at a node in each period and provides service to the refugees at that node. Each refugee should be served at least once in a predetermined number of consecutive periods. The problem is to locate the facilities in each period to minimize the total setup and travel costs of the mobile facilities, while ensuring the service requirement. We call this problem the multi-period mobile facility location problem with mobile demand (MM-FLP-MD) and prove its NP-hardness. We formulate a mixed integer linear programming (MILP) model and develop an adaptive large neighborhood search algorithm (ALNS) to solve large-size instances. We tested the computational performance of the MILP and the metaheuristic algorithm by extracting data from the 2018 Honduras Migration Crisis. For instances solved to optimality by the MILP model, the proposed ALNS determines the optimal solutions faster and provides better solutions for the remaining instances. By analyzing the sensitivity to different parameters, we provide insights to decision-makers.ArticlePublication Metadata only Reassessment and monitoring of loan applications with machine learning(Taylor & Francis, 2018-11-26) Boz, Z.; Danış, Dilek Günneç; Birbil, S. I.; Öztürk, M. K.; Industrial Engineering; DANIŞ, Dilek GünneçCredit scoring and monitoring are the two important dimensions of the decision-making process for the loan institutions. In the first part of this study, we investigate the role of machine learning for applicant reassessment and propose a complementary screening step to an existing scoring system. We use a real data set from one of the prominent loan companies in Turkey. The information provided by the applicants form the variables in our analysis. The company’s experts have already labeled the clients as bad and good according to their ongoing payments. Using this labeled data set, we execute several methods to classify the bad applicants as well as the significant variables in this classification. As the data set consists of applicants who have passed the initial scoring system, most of the clients are marked as good. To deal with this imbalanced nature of the problem, we employ a set of different approaches to improve the performance of predicting the applicants who are likely to default. In the second part of this study, we aim to predict the payment behavior of clients based on their static (demographic and financial) and dynamic (payment) information. Furthermore, we analyze the effect of the length of the payment history and the staying power of the proposed prediction models.ArticlePublication Metadata only A coordinated repair routing problem for post-disaster recovery of interdependent infrastructure networks(Springer, 2022-12) Atsız, Eren; Koyuncu, Burcu Balçık; Danış, Dilek Günneç; Sevindik, Busra Uydasoglu; Industrial Engineering; KOYUNCU, Burcu Balçık; DANIŞ, Dilek Günneç; Atsız, Eren; Sevindik, Busra UydasogluDisasters may cause significant damages and long-lasting failures in lifeline infrastructure networks (such as gas, power and water), which must be recovered quickly to resume providing essential services to the affected communities. While making repair plans, it is important to consider the interdependencies among network components to minimize recovery times. In this paper, we focus on post-disaster repair operations of multiple interdependent lifeline networks, which involve functional dependencies. We assume that each network component, whether damaged or not, becomes nonfunctional if it depends on another nonfunctional component, and it is recovered when all components that it depends on become functional. We introduce a post-disaster coordinated infrastructure repair routing problem, in which dedicated repair teams of each lifeline infrastructure travel through a road network to visit the sites with damaged network components. We present a mixed integer programming model that assigns repair teams to the sites and constructs routes for each team in order to minimize the sum of the recovery times for all network components. We develop a constructive heuristic and a simulated annealing algorithm to solve the proposed coordinated routing problem. We test the performance of the proposed solution algorithms on a set of instances that are developed based on two interdependent lifeline networks (e.g., power and gas). The computational results show that our heuristics can quickly find high-quality solutions. Our results also indicate that coordinating repair operations can significantly improve the overall recovery time of interdependent infrastructure networks.ArticlePublication Metadata only Integrating social network effects in the share-of-choice problem(Wiley, 2017-12) Danış, Dilek Günneç; Raghavan, S.; Industrial Engineering; DANIŞ, Dilek GünneçAccounting for social network effects in marketing strategies has become an important issue. Taking a step back, we seek to incorporate and analyze social network effects on new product development and then propose a model to engineer product diffusion over a social network. We build upon the share-of-choice (SOC) problem, which is a strategic combinatorial optimization problem used commonly as one of the methods to analyze conjoint analysis data by marketers in order to identify a product with largest market share, and show how to incorporate social network effects in the SOC problem. We construct a genetic algorithm to solve this computationally challenging (NP-Hard) problem and show that ignoring social network effects in the design phase results in a significantly lower market share for a product. In this setting, we introduce the secondary operational problem of determining the least expensive way of influencing individuals and strengthening product diffusion over a social network. This secondary problem is of independent interest, as it addresses contagion models and the issue of intervening in diffusion over a social network, which are of significant interest in marketing and epidemiological settings.