Measurement and Analysis of Online Social Networks
Post on 02-Jan-2016
DESCRIPTIONMeasurement and Analysis of Online Social Networks. By Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee Attacked by Ionut Trestian. Goals of the paper (1). Understanding social graphs for 2 things: Improving current systems Designing new applications - PowerPoint PPT Presentation
Measurement and Analysis of Online Social NetworksBy Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, Bobby Bhattacharjee
Attacked by Ionut Trestian
Goals of the paper (1)Understanding social graphs for 2 things:Improving current systemsDesigning new applications
Confirm properties of online social networks (e.g. power law, small world, scale-free)
Goals of the paper (2)
Detecting trusted or influential usersMitigate email spamImprove Internet searchDefend against Sybil attacks?
Goals of the paper (3)Large scale?If showing the same thing for a larger number of people is your main contribution then you could have written your paper in just a few lines.
More social networks?
More social networks ?How about networks with stronger identity enforcements?
The networks that you have a strong user population from are mostly content based (e.g. YouTube, LiveJournal, Flickr)(only 11% from Orkut)
Paper results (1)Symmetric links62.0% Flickr73.5% LiveJournal100.0% Orkut79.1% YouTube
Paper results (2)
Paper results (3)
Six degrees of separation (1)Classical result by Stanley MilgramShowed that any two individuals are separated by an average of six acquaintances
Another not so well known result is that most users are connected through a very small core of influential users the present paper calls them critical
Six degrees of separation (2)In the real world these hubs are real people
In a social network they just represent bits stored on a hard drive 101101101100000100101000101010101010000If you can mess with the bits that define a hub-type user you can mess with any of themHow are these critical?
Some final pointsThis study in no way seems to capture the actual dynamics of Social NetworksYou actually note that you observed a big difference between datasets collected at close times (two months)Even your future work on Ostra on leveraging thrust uses a not so suitable dataset that you acknowledge.
ConclusionsYour paper talks about random graphsThe whole paper seems random !Findings seem obvious and you acknowledge that they have been previously reportedIt seems more useful that you would spend your time tackling the goals - mitigating email spam, improving Internet search defending against Sybil attacks