Date post: | 20-Dec-2015 |
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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 systems
– Designing new applications
• Confirm properties of online social networks (e.g. power law, small world, scale-free)
Never happenedNever happened
Even the authors acknowledge that it has been shown in previous
studies
Goals of the paper (2)
• Detecting trusted or influential users
• Mitigate email spam
• Improve Internet search
• Defend against Sybil attacks
?These are awesome goals, I agree.
Why don’t you spend your time actually tackling them?
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 links
– 62.0% Flickr
– 73.5% LiveJournal
– 100.0% Orkut
– 79.1% YouTube
High degree of link symmetry
Basically means that if you are friend with someone he’s a friend with you …
Paper results (2)
Distributions of node indegree and outdegree are very similar
Isn’t this a clear consequence of the high link symmetry ?
Paper results (3)
Actually most results seem to be a consequence of the high link
symmetry property
Six degrees of separation (1)
• Classical result by Stanley Milgram
• Showed 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
101101101100000100101000101010101010000
• If you can mess with the bits that define a hub-type user you can mess with any of them
• How are these critical?
Some final points
• This study in no way seems to capture the actual dynamics of Social Networks
• You 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.
Conclusions
• Your paper talks about random graphs
• The whole paper seems random !
• Findings seem obvious and you acknowledge that they have been previously reported
• It seems more useful that you would spend your time tackling the goals - mitigating email spam, improving Internet search defending against Sybil attacks