In the lecture, we have learned about Finding Network Communities. I am wondering, what does the real world find their network communities and social bubble, and in which way will they break through their communities and get connect with others. By looking at the blog written by Andrew Lamb about “How to find communities online using social network analysis”.
In the blog, Lamb had listed a famous example create by Lada Adamic’s analysis of US political bloggers during 2004. Lada concludes that the graph clearly shows Blue node (Democrat) only has very little communication with the Red node (Republican). Due to the bloggers’ political habit, they only have a very small chance to be friends with another party. However, this is not all of the social networks that look like the above one has such a sharply divided image.
Most visualizations created by any social media application did not have a clear border but will group people by their interests and concerns (Lamb, 2013). The one in the middle of the graph tends to be the people who have more power over the flow of information. In the blog, Lamb had given an example of how to reach to Econsultancy network on Twitter. He defined as finding the people who had high centrality or betweenness as the first step. It is very interesting that this theorem is exactly the same as Graph Partitioning as we discuss in class whereby find the bridge with the highest betweenness will divide the graph evenly. On the social web, it also means making friends with people who have the largest connection will also give you the ability to reach out to everyone on the web.
By concluding the blog of Andrew Lamb with the lecture material we have, it is clear that the technique we learn also implied to the real-world social network and it could also help us to easily find the online community with proper analysis technology and terminology we learn in this class.
Reference:
Lamb, Andrew. How to Find Communities Online Using Social Network Analysis. 4 Nov. 2013, econsultancy.com/how-to-find-communities-online-using-social-network-analysis/.