Abstract
In real-life social networks, the decisions of individual actors are often influenced by multiple sources of information, whose relative influence depends on several factors, in much the same way as many real world networks, such as the spread of viruses, or the spreading of a new product’s reputation through a given human population. Previous attempts to model social networks have focused on single-diffusion processes. However, real social networks are usually more complicated, and attempting to model them with single-diffusion processes often fails to capture higher-order effects seen in the real world. Multiple-diffusion processes have to take into account not only multiple sources of information, but also multiple types of information source, where each of the information sources may potentially contradict one another. Complex calculations involving conflicting information sources must rely on heuristics to reduce the execution time. This study provides a multi-objective optimization algorithm for solving performance problems using a creative and heuristic algorithm. The results provide a motivation to utilize the algorithm in multiple diffusion and conflicting-information problems of social networking.
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Lee, IH., Lin, JH., Wu, CC. (2015). A Novel Multi-objective Optimization Algorithm for Social Network. In: Wang, L., Uesugi, S., Ting, IH., Okuhara, K., Wang, K. (eds) Multidisciplinary Social Networks Research. MISNC 2015. Communications in Computer and Information Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-48319-0_3
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DOI: https://doi.org/10.1007/978-3-662-48319-0_3
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