Online Social Networks Can Be Tipped By Less Than 1% of Their Population

An anonymous reader writes "A new algorithm developed by researchers at West Point seems to break new ground for viral marketing practices in online social networks. Assuming a trend or behavior that spreads in an online social network based on the classic 'tipping' model from sociology (based on the work of Thomas Schelling and Mark Granovetter), the new West Point algorithm can find a set of individuals in the network that can initiate a social cascade ? a progressive series of 'tipping' incidents ? which leads to everyone in the social network adopting the new behavior. The good news for viral marketers is that this set of individuals is often very small ? a sample of the Friendster social network can be influenced when only 0.8% of the initial population is seeded. The trick is finding the seed set. The algorithm is described in a paper to be presented later this summer at the prestigious IEEE ASONAM conference."

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