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The Spread of Innovations in Social Networks

“The Spread of Innovations in Social Networks” is an article written by Andrea Montanari and Amin Saberi that focuses on two specific social network models.  One of the main purposes of this article is to contrast the epidemic network model with the game-theoretic network model.  The epidemic theory is based on the belief that innovations spread as people are introduced to a new innovation by others in their personal social network.  In this model, innovations spread much like a disease: once one person becomes infected, it is highly possible that he will infect another in close proximity to him.  This article not only thoroughly defines and explains the epidemic model, but also claims that not all information, inventions, and ideas spread simply due to influence of others.

To create a foundation for this argument, the article highlights the key elements that are present in an epidemic model and contrasts these with those of the game-theoretic model.  In an epidemic model, it is expected that ideas spread rapidly in portions of the model that have a high density of interconnections.  Also, connections that extend over a significant distance help spread the innovation to new areas faster.  Finally, the epidemic model is based on the belief that nodes with a high density of connections are essential for fast and thorough spreading of new ideas and innovations.

These specific patterns that are related to epidemic models differ from the patterns that define the game-theoretic model. Instead of large distances being beneficial, the game-theoretic model favors the spreading of innovations in neighboring regions.  In addition, the most apparent contrast between models is that the game-theoretic model suggests that nodes with a large amount of interconnections actually slow down the spread of influence. This belief is supported in the article by evidence that suggests that people do not only accept new ideas and innovations because friends have. However, people accept new things based on the payoff that person will receive from these innovations. The incentive of utility rather than simply blending with the community supports the idea that highly connected nodes slow down the spreading process. This is because a new idea, appliance, or service can have a higher payoff for a person if it is being used by many friends, family, and co-workers. If a person has dozens of strong connections, it will take a longer time for that person to have a large enough incentive to conform than a person who only has a few strong connections.

The presentation of these two contrasting network models is related to many of the ideas and concepts that were discussed in lecture.  The visual interpretations of nodes and social networks presented during lecture were vital for the comprehension of this article.  By understanding the difference between highly connected nodes and more secluded nodes, one of the most influential points of this article, regarding the contrast between models, could be better comprehended. Furthermore, the examples and definitions associated with game theory from lecture appeared consistently throughout the article.  The knowledge that people often adopt an innovation based on the payoff generated by this decision was highlighted by the argument of this article.  Also, the examples of game theory used in the article to present evidence were comparable to the concepts taught by the prisoner’s dilemma presented in lecture.  The idea that an equilibrium can be reached in a situation involving payoffs was introduced in lecture through the prisoner’s dilemma and presented in the article through other games as well.

Montanari, Andrea, and Amin Saberi. “The Spread of Innovations in Social Networks.” PNAS, 12 Nov. 2010. Web. 05 Sept. 2012. <http://www.pnas.org/content/107/47/20196.full>.

~Alice

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