One of the most graphically unpredictable areas of web is mapping out interests and influence from social media. The data changes so sporadically that timeline specific indicators become often semantically and contextually flawed in their results. Some examples of indicators of social media analytics applications come from sources like PeerIndex, Klout, Bugscore, and Q score. Unfortunately, the more popular one is Klout which flagrantly goes against Data Protection Laws. PeerIndex is also a very flawed model for influence graphs and although it stresses privacy it is probably worse in measure for results compared to Klout not to mention that they also have a very bad service model. Perhaps, the most non-effective influence graph out there for marketing. However, the more interesting and focused options is FaceBook OpenGraph. Apart, from that there is the Google Knowledge Graph which is in a different context of its own for graph search, but very useful at the same time.
Source of reading material to find out more about such social graphing perspectives:
Network, Crowds and Markets: Reasoning About a Highly Connected World
Source of reading material to find out more about such social graphing perspectives:
Network, Crowds and Markets: Reasoning About a Highly Connected World
