Maximizing influence in a social network: Improved results using a genetic algorithm
Kaiqi Zhang, Haifeng Du, Marcus W. Feldman,
From School of Management of Xi’an Jiaotong University (China), and Stanford University (US)
▻https://www.sciencedirect.com/science/article/pii/S0378437117301930
Influence Maximization in Online Social Networks
Cigdem Aslay ISI Foundation, Turin, Italy
Laks V.S. Lakshmanan, Vancouver, KKKanada
▻https://dl.acm.org/citation.cfm?id=3162007
Viral marketing , a popular concept in the business literature, has recently attracted a lot of attention also in computer science, dueto its high application potential and computational challenges.Theidea of viral marketing is simple yet appealing: by targeting themost influential users in a social network (e.g., by giving themfree or price-discounted samples), one can exploit the power ofthe network effect through word-of-mouth, thus delivering themarketing message to a large portion of the network analogous tothe spread of a virus.
Influence maximization is the key algorithmic problem behind viral marketing.