Social Graphs Spread Information

 Social Graphs Spread Information

Beyond linking people within a social network, the relationships that are present within a social graph play an important role in the spread of information throughout that social graph, and hence play a direct role in the sharing of content across a social network. Looked at another way, without sharing, a social network is a largely theoretical construct: What difference do a thousand connections make if nothing of value is flowing between them? Consider, for example, the value to you, personally, of a network that you may have joined without understanding why except perhaps that everyone else was joining it and as a result rarely find yourself using it: You have connections within this network, but of what use are they? As an example, when I first joined Twitter I did not “get it.” Half of my motivation for joining was “everyone else” I knew had already joined. That’s when Twitter made sense when I was able to utilize my social graph as it existed within Twitter in ways that benefited me and benefited the growing Twitter community around me. Again, the takeaway is that only with a meaningful social graph only with connections between people with shared interests, common values, or aligned purposes, as examples do the social networks that people may belong to become relevant. The applications built around the direct use of the social graph are important: I spoke with Rapleaf’s person is part of Rapleaf’s marketing team about applications that mine or otherwise tap the information exposed through the study of a particular social graph. He noted applications ranging from driving participation in online gaming in one application, players with more than five friends in the game were significantly more engaged than those with four or less to the observation that the spread of movie reviews is directly related to the respective graphs of the individuals who publish reviews. All of this suggests the value of knowing, through measurement, which is connected to whom, and how these connections can therefore be used to encourage additional connections. Using Rapleaf a program that identified the friends of prospective students who were already students themselves all done with explicit permission resulted in proof that a significant increase in likelihood of new student conversion is associated with Having a friend who is also a student.

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