Download - Measurement and Analysis of Online Social Networks

Measurement and Analysis of Online Social Networks

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

Presentation by

Shahan Khatchadourian

Supervisor: Prof. Mariano P. Consens


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Focus

• graphs of online social networks– how they were obtained– how they were verified

• how measurement and analysis was performed

• properties of obtained graphs

• why these properties are relevant


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Why study the graphs?

• important to improve existing system and develop new applications– information search– trusted users

• what is the structure of online social networks• what are different ways to examine a social

network when complete data is not available?• how do they compare with each other and to

the Web?


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Which graphs?

• Flickr, YouTube, LiveJournal, and Orkut

• All are directed except for Orkut

• Weakly Connected Component (WCC)

• Strongly Connected Component (SCC)


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How are the graphs obtained?

• API– users– groups– forward/backward links

• HTML Screen Scraping


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Summary of graph properties

• small-world

• scale-free

• correlation between indegree and outdegree

• large strongly connected core of high-degree nodes surrounded by small clusters of low-degree nodes


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Crawling Concerns - Algorithms

• BFS and DFS

• Snowball method: underestimates number of low-degree nodes. In social networks, they underestimate the power-law coefficient, but closely match other metrics such as overall clustering coefficient.


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Crawling Concerns – FW links

• cannot reach entire WCC


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How to Verify Samples

1. Obtain a random user sample– LJ: feature which returns 5,000 random users– Flickr: random 8-digit user id generation

2. Conduct a crawl using these random users as seeds

3. See if these random nodes connect to the original WCC

4. See what the graph structure of the newly crawled graph compares to original


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Crawling Concerns – FW links

• no effect on largest WCC


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Crawling Concerns – FW links• increasing the size of the WCC by starting at

a different seed


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Site YT Flickr LJ Orkut

Users(mill) 1.1 1.8 5.2 3

Links(mill) 4.9 22 72 223

symmetry 79.1% 62.0% 73.5% 100.0%

Access (FW: Forward-only)

(SS: HTML screen-scraping)

API

(users only)

FW

SS for group info

API

(users + groups)

FW

API

(users + groups)

FW + BW

SS for users + groups


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Link Symmetry

• even with directed links, there is a high level of symmetry

• possibly contributed to by informing users of new incoming links

• makes it harder to identify reputable sources due to dilution

• possible sol: who initiated the link?


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Power-law node degrees

• Orkut deviates:– only 11.3% of network reached (effect of

partial BFS crawl – Snowball method)– artificial cap of user’s number of outgoing

links, leads to a distortion in distribution of high degrees

• differs from Web


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e.g. analysis of top keywords


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Spread of Information


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Power Law affectors

• services, accessibility, features

mobile users

100 100

10-810-8

11 10000 10000


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Correlation of indegree and outdegree

• over 50% of nodes have indegree within 20% of their outdegree


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Path lengths and diameter

• all four networks have short path length

• Broder et al noted if Web were treated as undirected graph, path length would drop from 16 to 7, so what?


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Link degree correlations

• JDD: joint degree distribution

• mapping between outdegree and average indegree of all nodes connected to nodes of that outdegree

• YouTube different due to extremely popular users being connected to by many unpopular users

• Orkut shows bump due to undersampling


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Joint degree distribution and Scale-free behaviour

undersamplingof low-degreenodes

celebrity-drivennature

cap on links


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Densely connected core

• removing 10% of core nodes results in breaking up graph into millions of very small SCCs

• why an SCC? directed links matter for actual communication• graphs below show results as nodes are removed starting with

highest-degree nodes (left) and path length as graph is constructed beginning with highest-degree nodes(right)

Sub logarithmic growth


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Tightly clustered fringe

• based on clustering coefficient

• social network graphs show stronger clustering, most likely due to mutual friends

Possibly because personal content is not shared


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Groups

• group sizes follow power-law distribution

• represent tightly clustered communities


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Groups

• Orkut special case maybe because of partial crawl


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Node Value Determination

1. Directed Graph, current model• 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• unhealthy in case of spam or viruses

• in order for a user to send spam, they have become a more important node, amass friends


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2. Link Initiator, requires temporal information

• if user A requests a link with user B, does that mean that user B is more important?

• even though graphs have a high level of link symmetry, this additional information can offset this symmetry

• unfortunately, examined graphs do not have temporal information

Node Value Determination


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Trust

• lendingclub.com, Facebook application• people are more willing to lend money to

friends who are linked through a short path• people are more willing to pay back those

who are linked through a short path• no indication of whether this actually works

– does trust increase as degree increases?– what credit rating and JDD does a person

have to get a good interest rate?


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Thank you

shahan@cs

Download - Measurement and Analysis of Online Social Networks

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