Maximizing influence in a social network : Improved results using a genetic algorithm


  • 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)

    Influence Maximization in Online Social Networks
    Cigdem Aslay ISI Foundation, Turin, Italy
    Laks V.S. Lakshmanan, Vancouver, KKKanada

    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.