Browsing by Author "Sinnl, Markus"

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Benders Decomposition Algorithms for Minimizing the Spread of Harmful Contagions in Networks
(Pergamon-elsevier Science Ltd, 2024) Sinnl, Markus; Tanınmış, Kübra; Güney, Evren; Aras, Necati
The 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.
Citation - WoS: 31
Citation - Scopus: 33
Large-Scale Influence Maximization Via Maximal Covering Location
(Elsevier, 2020) Güney, Evren; Ruthmair, Mario; Sinnl, Markus; Leitner, Markus
Influence 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.