https://hdl.handle.net/20.500.12723/3145 Articles Collection Thu, 09 Apr 2026 01:49:30 GMT 2026-04-09T01:49:30Z https://hdl.handle.net/20.500.12723/3576 Exploring Spreaders in a Retweet Network: A Case from the 2023 Kahramanmaraş Earthquake Sequence Adak, Zeynep; Çetinkaya, Ahmet Two massive earthquakes struck Kahramanmaraş district of Türkiye on 6 February 2023, leaving loss of life and damage in a catastrophic scale. Many blamed the government for its inefficiency in dealing with the disaster. #devletyok (there is no government) was a hashtag used in the aftermath in social networking sites. We analyze the retweet network around the hashtag on 24th February, two weeks after the disaster, and aim to extract topological characteristics of the network, the influential spreaders in the network and the source of the diffusion. We make use of centrality measures, the HITS algorithm, PageRank algorithm and the k-shell decomposition in order to detect the influential spreaders. The social network analysis here is different from much of the previous research in that we explore the central roles in an information diffusion on a network, where all nodes are active, representing an already diffused information. In-degree centrality, betweenness centrality and HITS algorithm provide useful results in detecting spreaders in our network, while closeness centrality, PageRank and k-shell decomposition supply no additional knowledge. We figure out three nodes in the network with central roles in the diffusion, one being the source node. Checking the account of this source node reveals an anonymous user, who does not declare his/her identity. The study here has useful future implications for political and governmental studies. Moreover, the procedure applied to detect influential spreaders has many potential use cases in other fields such as marketing and sociology. Mon, 01 Jan 2024 00:00:00 GMT https://hdl.handle.net/20.500.12723/3576 2024-01-01T00:00:00Z https://hdl.handle.net/20.500.12723/3146 An Ant Colony Optimization Approach For The Proportionate Multiprocessor Open Shop Adak, Zeynep; Övül Arıoğlu, Mahmure; Bulkan, Serol Multiprocessor open shop makes a generalization to classical open shop by allowing parallel machines for the same task. Scheduling of this shop environment to minimize the makespan is a strongly NP-Hard problem. Despite its wide application areas in industry, the research in the field is still limited. In this paper, the proportionate case is considered where a task requires a fixed processing time independent of the job identity. A novel highly efficient solution representation is developed for the problem. An ant colony optimization model based on this representation is proposed with makespan minimization objective. It carries out a random exploration of the solution space and allows to search for good solution characteristics in a less time-consuming way. The algorithm performs full exploitation of search knowledge, and it successfully incorporates problem knowledge. To increase solution quality, a local exploration approach analogous to a local search, is further employed on the solution constructed. The proposed algorithm is tested over 100 benchmark instances from the literature. It outperforms the current state-of-the-art algorithm both in terms of solution quality and computational time.; Telif hakları gereğince yayın erişime kapalıdır. Yayın yayıncı tarafından erişime açık ise bağlantılar kısmından ulaşılabilmektedir.; 23.09.2021 Fri, 01 Jan 2021 00:00:00 GMT https://hdl.handle.net/20.500.12723/3146 2021-01-01T00:00:00Z