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