1

Measurement and Analysis of Online Social Networks

A. Mislove, M. Marcon, K Gummadi, P. Druschel, B. Bhattacharjee

Presentation byYong Wang

(Defense side)

My general opinion

• This is a brilliant paper.

2

Title

• Let’s recall the title of this course.

3

“There are no

accidents”

Author• Who is this guy?

Alan Mislove

At least six papers on OSN published within two years to top-class conferences, like WWW, IMC, WOSN, NSDI..

We will read two of them in the next two months

This paper- 16 citations already in one year

From Rice Univ. Social relationship

4

Contributions• Introduce what online social networks are

definition section 2

• Measure online social networks at scale

data section 4

• Introduce static structural properties

results section 5

• Explain why study online social networks?

impact section 5 + section 6

5

What are (online) social networks?

• Social networks are graphs of people

Graph edges connect friends

• Online social networking Social network hosted by a Web site

Friendship represents shared interest or trust

Online friends may have never met

6

Data -- Measure online social networks at scale

• This paper presents a large-scale measurement study and analysis on four online social networks containing over 11.3 million users and 328 million links.

7

8

Site YT Flickr LJ Orkut

Users(mill)

1.1 1.8 5.2 3

Links(mill)

4.9 22 72 223

Traffic ranking in Alexa

3 34 88 102

Coverage Rich media --video

Rich media – photo

Blog “Pure”OSN

Data are representative

How large the scale is?

9

Paper Site proportion

This paper Orkut 11.3%

Analysis of Topological Characteristics of Huge Online

Social Networking Services (WWW’07)

Orkut 0.3%

This paper LiveJournal 95.4%Group Formation in Large Social

Networks: Membership, Growth, and Evolution (KDD’06)

LiveJournal 0.08%

10

Why study the graphs?• important to improve existing system and

develop new applications

– information search

• Web search: PeerSpective [HotNets’06]

– trusted users

• Trust can be used to solve security problems

• Multiple identity attacks: SybilGuard [SIGCOMM’06]

• Spam: RE [NSDI’06]

• Ostra: thwart unwanted communication [NSDI’08]

• Understanding network structure is necessary first step

Information searchLocating content

• Comparison between Google and OSN

• How Google comes and works?

11

Search on OSN • The integration of search engines and

online social networks could enable queries such as

• "Has any of my acquaintances been on holidays in New Zealand?" or

• "Recent articles on hypertext authored by people associated with Ted Nelson".

12

Results+Impacts

• Link symmetry

• Power-law node degrees

• Correlation of indegree and outdegree

• Path lengths and diameter

• Link degree correlations

• Densely connected core

• Tightly clustered fringe

• Groups

Link symmetry

• Social networks show high level of link symmetry

14

In the WEB (CNN- “a dancing queen” in the web) Things are different in OSN due to reciprocation

In the OSN world, “the dancing queen” may place a link pointing back to other “gentlemen”, although not 100%..

likelihood is much higher 

Implications of high symmetry

Implications is that ‘hubs’ become ‘authorities’

May impact search algorithms (PageRank, HITS)

Open a research direction for others, e.g.

“The Karma of Digg: Reciprocity in Online Social Networks” by E. Sadlon et al. in 2008

15

Power-law node degrees

16U.S. highways U.S. Airlines

Power-law node degrees• In the WEB (CNN vs. personal webpages)

• In the OSN- power-law. as well, after all, it is second life

17

• In the WEB, the indegree and outdegree power-law exponents differ significantly• In the OSNs, the power-law exponents for the indegree and outdegree distributions in each of the social networks are very similar

Power-law node degrees• The differences show that : In the WEB, the

incoming links are significantly more concentrated on a few high-degree nodes than the outgoing links

18

In all social networks, distributions of incoming and outgoing links across the nodes are very similar.

19

Implications of Power-law degrees

• Realize the structure of OSN --- power-law.

• nodes with many incoming links (hubs) have value due to their connection to many users

• it becomes easy to spread important information to the other nodes, e.g. DNS

• in order for a user to send spam, they have to become a more important node, amass friends. introduced at • “SybilGuard : [SIGCOMM’06]” and

• “Ostra : [NSDI’08]”

20

Correlation of indegree and outdegree

• In WEB, most nodes have considerably higher outdegree than indegree, while a small fraction of nodes have significantly higher indegree than outdegree. (CNN vs. personal webpages)

• In social networks, the nodes with very high outdegree also tend to have very high indegree

• The famous people who know lots of people also is known by lost of people

PW CNN

OSN

Implications of Correlation of indegree and outdegre

• The high correlation between indegree and outdegree in social networks can be explained by the high number of symmetric links

• The high symmetry may be due to the tendency of users to reciprocate links from other users who point to them.

21

Search information : makes it harder to identify reputable sources due to dilutionpossible sol: who initiated the link?

22

Path lengths and diameter• all four networks have short path length

from 4.25 – 5.88

• six degrees of separation

Facebook, 4.2 million for Octorber 2007, 6.12 fromhttp://blog.paulwalk.net/2007/10/08/no-degrees-of-separation/

23

Implications of Path lengths and diameter

The small diameter and path lengths of social networks are likely to impact the design of techniques for finding paths in such networks

24

Link degree correlations• high-degree nodes tend to connect to other high-degree nodes ? OR

• high-degree nodes tend to connect to low-degree nodes ?

• In real society: the former theory is true.

• By virtue of two metrics: the scale-free metric and the assortativity.

• Suggests that there exists a tightly-connected “core” of the high-degree nodes which connect to each other, with the lower-degree nodes on the fringes of the network.

• The next question: How big the core is

25

Implications of Link degree correlationsSpread of Information

“A Measurement-driven Analysis of Information Propagation in the Flickr Social Network” [WWW’ 09]

26

Densely connected core

• the graphs have a densely connected core comprising of between 1% and 10% of the highest degree nodes such that removing this core completely disconnects the graph.

Sub logarithmic growth

Implications of densely connected core

• Network contains dense core of users

Core necessary for connectivity of 90% of users

Most short paths pass through core

Could be used for quickly disseminating information

• So 10% at core

• What about remaining nodes (90% at fringe)

27

28

Tightly clustered fringe• Clustering Coefficient of a network:

How many of your friends are also friends themselves?

• social network graphs show stronger clustering, most likely because: people tend to be introduced to other people via mutual friends, increasing the probability that two friends of a single user are also friends.

Are the fringes more clustered?

The clustering coefficient is higher for nodes of low degree

Implications of Tightly clustered fringe

• Fringe is highly clusteredUsers with few friends form mini-cliques

Similar to previously observed offline behavior

Could be leveraged for sharing information of local interest

29

30

Groups• group sizes follow power-law distribution

• the members of smaller user groups tend to be more clustered than those of larger groups

31

Groups

• Low-degree nodes tend to be part of very few communities, while high-degree nodes tend to be members of multiple groups.

Implications of Groups

• “To Join or Not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles” [WWW’ 09]

32

• Finally, Give details and reasons for all deviations , that is good

33

What does the structure look like

the networks contain a densely connected core of high-degree nodes;

and that this core links small groups of strongly clustered, low-degree nodes at the fringes of the network.octopus

Two stories

• This paper shows its brilliance in the same way

35