In this paper, the authors seek to understand social networks by building an effective tool called SocialAction to analyze social networks, without marginalizing the relationships between elements or the elements themselves. To make their analysis tool perform better, they focus on systematic yet flexible exploration. SocialAction is built based on framework that supports rank-by-feature. This seems to be more effective in analyzing multidimensional data. I think that any social network has so many facets that it could be easily considered as a case of high dimensional visualization like discussed in class last week. Similar techniques like reduction or clustering could prove effective in such a case. Reading further on, a similar strategy seems to be adopted by the authors. The key point where SocialAction differs from other SNA tools is the "systematic" way in which the network, the nodes and the relationships are discovered. Then the authors proceed to aggregate and rank by filtering, finding subgroups.
Today, social networks have come a long way. Mobile social networking is becoming popular. This mobile feature is built on wireless or ad hoc networks. Also, a lot of mash-ups are prevalent today. A user'r profile or activity on one social network affects that user's activity/updates on another. So to conclude, I am wondering how these mash ups and the dynamic or ad hoc nature of the network underneath would affect the performance of SocialAction.
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