An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement Chao, Xiangrui Herrera Viedma, Enrique Herrera Triguero, Francisco Large-scale decision-making Consensus reaching Dual social network Preference classification Minimum consensus cost The authors thank the editor and the anonymous referees for their valuable comments and insightful recommendations. This work was supported in part by grants from the National Natural Science Foundation of China (#71874023, #71771037, #71971042, #71910107002, and #71725001) and supported by the Spanish State Research Agency under Project PID2019-103880RB-I00/AEI/10.13039/501100011033. Urban resettlement projects involve a large number of stakeholders and impose tremendous cost. Developing resettlement plans and reaching an agreement amongst stakeholders about resettlement plans at a reasonable cost are some of the key issues in urban resettlement. From this perspective, urban resettlement is a typical large-scale group decision-making (GDM) problem, which is challenging because of the scale of participants and the requirement of high consensus levels. Observing that residents who are affected by a resettlement project often have tight social connections, this study proposes a framework to improve the consensus reaching and uses the minimum consensus cost to reduce the total cost for urban resettlement projects with more than 1000 participants. Firstly, we construct a network topology that consists of two layers to deal with incomplete social relationships amongst large-scale participants. An inner layer consists of participants whose preference similarities and trust relations are known. Meanwhile, an outside layer includes participants whose trust relations cannot be determined. Secondly, we develop a classification method to classify participants into small subgroups based on their preference similarities. We can then connect the participants whose trust relations are unknown (the outside layer) with the ones in the inner layer using the classification results. To facilitate effective consensus reaching in large-scale social network GDM, we develop a threestep approach to reconcile conflicting preferences and accelerate the consensus process at the minimum cost. A real-life urban resettlement example is used to validate the proposed approach. Results show that the proposed approach can reduce the total consensus cost compared with the other two practices used in the actual urban resettlement operations. 2021-10-19T07:08:34Z 2021-10-19T07:08:34Z 2021-06-18 info:eu-repo/semantics/article Xiangrui Chao... [et al.]. An efficient consensus reaching framework for large-scale social network group decision making and its application in urban resettlement, Information Sciences, Volume 575, 2021, Pages 499-527, ISSN 0020-0255, [https://doi.org/10.1016/j.ins.2021.06.047] http://hdl.handle.net/10481/70962 10.1016/j.ins.2021.06.047 eng http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España Elsevier