Review: Measurement and Analysis of Online Social Networks October 4, 2007Posted by shahan in online social networks.
Tags: online social networks
Measurement and Analysis of Online Social Networks, A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, S. Bhattacharjee. Internet Measurement Conference (IMC) 2007.
An in-depth study of online social network graphs is performed for Flickr, LiveJournal, Orkut, and YouTube. Typical graph properties are indicated and compared amongst each other such as: power-law coefficients, degree correlation, link symmetry, paths, and fringe clusters, as well as some brief analysis on groups. The authors indicate that this information will be useful to enhance existing and develop new applications and algorithms by comparing them to the web. There is a lack of practical application of the results however with little or no future direction indicated.
Amongst the multitude of values presented, I found the link symmetry to be one of the most interesting portions of the paper. While the authors speculate that core nodes with a high degree can be useful in the transmission of information, they can also be detrimental in the case of spam or viruses. On the web, PageRank considers pages with many incoming links and few outgoing links to be authoritative and a source of information. Conversely, pages with many outgoing links with few incoming links are considered active and are not sources of information. Using this type of model allows PageRank to effectively identify pages that contain useful information; however, due to link symmetry in an online social network, this does not apply.
A suggestion would be to examine the destination of a link request such that if user A request a connection to user B first, then user B can be deemed more important. This would be useful in the case of YouTube, where the average indegree of friends connected to nodes of high outdegree is low, thus the “celebrity-driven” nature of the content (Figure 6). Considering the link destination as being more valuable is one way to offset the symmetric nature of links. This model is akin to voting for someone when you request a link to them, your vote being acknowledged through link reciprocation. In the case of lack of temporal information, we can infer more important nodes as we know the resultant outdegree versus average indegree of friends graph . A benefit of having the temporal information is also to quash the potential infiltrator who wishes to spread a virus, thus instead of allowing a node to become part of a hub by having many links, they would be trusted only if many other nodes have a desire to connect to them first.
Despite several well-explained graphs of differences due to snowball sampling or link caps, there is a lack of conclusion in the paper. It sets the stage very well for the exploration of questions and answers, with the data itself available for download, for better solutions for online social network-based trust and information retrieval techniques.