Finding influentials in social networks using evolutionary algorithm
作者： Michał Weskida , Radosław Michalski
DOI: 10.1016/J.JOCS.2018.12.010
关键词: Machine learning 、 Social influence 、 Artificial intelligence 、 Heuristics 、 Evolutionary algorithm 、 Maximization 、 Transferability 、 Linear threshold 、 Set (psychology) 、 Computer science 、 Statement (computer science)
摘要: Abstract The social influence maximization problem is an important research topic for many years since it has a tremendous impact on society. As can be maximized purposes, such as marketing, politics, spreading innovations, there are stakeholders interested in progress this area. been shown, most settings finding optimal seed set NP-hard problem, why various heuristics started to emerge the statement of problem. In work, we explore applicability evolutionary algorithm maximization. experiments conducted using real and artificial networks linear threshold model show that approach offers not only speed accuracy. Also, some other interesting features have found, transferability its parameters datasets. Summarizing results, was observed does lose performance when limiting time by factor two datasets, obtained high correlation ranked parameters’ sets used algorithm, typically around 0.9. Overall, these combined make direction
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