A closer look at Online Social Networks (OSNs)

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Universidade Lusófona, IANLab meeting

Waldir Moreira

15/02/2011

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Agenda

Why study Online Social Networks?

Objects of Study

Crawling on OSNs

Identified Structural Properties

Summary

Why study Online Social Networks?

Evaluate current systems (Improvements)

Design future OSN-based systems

– Role in personal and commercial online interaction

– Location and organization of data and knowledge

Understand impact of OSNs on Internet

– OSNs are popular and bandwidth-intensive

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Why study Online Social Networks?

Detect trusted/influential users

– Trust on each other (Email Spam)

– Common interests (Improve Internet search)

Routing ☺

– Increase reliability of used links

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Objects of Study

“Pure” social networking site

– Orkut: finding and connecting users

For publishing, organizing, and locating content

– Flickr

– YouTube

– LiveJournal

Most popular social networking sites and allow to view links out of any user

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Crawling OSNs

Publicly accessible information

Automated scripts on a cluster of 58 machines

Breadth-first search (BFS)

– Retrieve the list of not-visited friends for a user

– Add it to the list of users to visit

– Continue until exhaust the list

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Identified Structural Properties

Significant degree of link symmetry even in OSNs with directed links

– Increases overall connectivity and reduces its diameter

– Dilutes importance of reputable sources

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Identified Structural Properties

Power-law node degrees

– Consistent behavior with a power-law network

– Majority of nodes have small degree, and few nodes have significantly higher degree

– Distribution of outgoing links is similar to that of incoming links

– Active users also tend to be popular

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Identified Structural Properties

Power-law node degrees

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Identified Structural Properties

Path lengths and diameter

– Social networks have significantly shorter average path lengths

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Identified Structural Properties

Link degree correlations

– How often nodes of different degrees connect to each other

– knn, mapping between outdegree and the average indegree of all nodes connected to nodes of that outdegree.

– Trend for high-degree nodes to connect to one another is observed in all networks except YouTube

– Forming a “core” of the network.

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Identified Structural Properties

Link degree correlations

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Identified Structural Properties

Densely connected core

– Necessary for the connectivity of the network

– Strongly connected with a relatively small diameter

– Densely connected core comprising 1% to 10% of the highest degree nodes

– May have implications for information flow, for trust relationships, and for the vulnerability

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Identified Structural Properties

Tightly clustered fringe

– Highly-clustered local neighborhoods outside core

– Significant clustering among low-degree nodes

– People tend to be introduced via mutual friends

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Identified Structural Properties

Groups (shared interests)

– Users in a group not necessarily have a link to each other (SociaCast assumption)

– User groups represent tightly clustered communities

– Members of smaller user groups tend to be more clustered than those of larger groups

– Low-degree nodes are part of very few communities, while high-degree nodes are members of multiple groups

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Summary

Validate– Power-law (degree distribution)– Small-world (small diameter and high clustering)– Scale-free (high-degree nodes tend to be connected

to other high-degree nodes)

Observe a high degree of reciprocity in directed user links, leading to a strong correlation between user indegree and outdegree.

Large, strongly connected core of high-degree nodes, surrounded by many small clusters of low-degree nodes.

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References

[Mislove et al. 2009] A. Mislove, P. Druschel, M. Marcon, B. Bhattacharjee, and K. P. Gummadi, "Measurement and Analysis of Online Social Networks." IMC'07, 2007.

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Thanks!

Extra

http://fellows-exp.com/

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