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Analysing Social Networks Via the Internet. Bernie Hogan PhD Candidate, Department of Sociology Research Coordinator, NetLab. “As we may think”. Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them… Vannevar Bush, 1945.

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analysing social networks via the internet

Analysing Social Networks Via the Internet

Bernie Hogan

PhD Candidate, Department of Sociology

Research Coordinator, NetLab

as we may think
“As we may think”
  • Wholly new forms of encyclopedias will appear, ready made with a mesh of associative trails running through them…
    • Vannevar Bush, 1945
60 years later
60 years later…
  • We have no shortage of associative trails. But it is not confined to information…
  • “When computer networks link people as well as machines, they become social networks” (Wellman, et al. 1996)
why do networks matter
Why do networks matter?
  • Google’s succeeded through a social network algorithm.
  • MySpace and Facebook are the largest explicit social networks ever created.
  • We can show how the rich get richer “Preferential attachment” (Barabasi and Albert 1998),
  • And how everyone is only ‘six degrees’, apart (Milgram 1967; Watts 2001).
the oracle of kevin bacon the original online network
The Oracle of Kevin Bacon: The Original Online Network

The Importance of

Being Earnest

Where the Truth Lies

84 Charing

Cross Road

A Few Good Men

Mission:

Impossible II

what are networks
What are networks?

Relationships between “actors”:

  • Friendships
  • Partnerships
  • Hyperlinks

Information about “actors”:

  • People
  • Businesses
  • Webpages

Plus

nodes
Nodes
  • Generally constrained to well defined types.
    • People to people (not to orgs and teams).
  • More than one type are included in ‘affiliation networks’
    • Linking people as one set to events as another set.
links can be
Links can be:
  • Directed links [arcs] (from me to you)
  • Undirected links [edges] (me and you)
  • Valued (I sent 3 messages to you)
  • Signed (I like him & I dislike her)
  • Multiplex (l link to her blog, know her email and on her MySpace page)
some network types
Some Network Types

Users of a

web forum

Subset of

political blogs

Friend pages

on MySpace

where to find networks online
Where to find networks online?

Social networking

Email

Social news

Web links

Blogs

Message boards

Games

Instant messengers

capturing this data online
Capturing this data online
  • Scraping pages
    • Using scripting languages (python, perl)
    • Using scraping software
  • APIs (Application Program Interface)
    • Again using scripting languages
    • Out-of-the-box software
    • Online applications
  • More on this tomorrow!!
analysing data
Analysing Data
  • Software Applications
    • UCInet: powerful, social-science oriented, quirky interface
    • Pajek: powerful, strange interface, comprehensive
    • Others (Egotistics, NetMiner, Visualyzer, NetWorkBench)
  • Software Environments
    • JUNG (Java Universal Network Graphing Package)
    • R (SNA package)
    • iGraph (Python)
common metrics i centrality
Common metrics I: Centrality
  • Who is the most connected?
    • Simple question, complex answer

Degree:

Number of links

3

Betweenness:

Shortest paths

PageRank:

Links to high degree

common metrics ii sub groups
Common metrics II: Sub-groups
  • Interested in group structure
    • Again, many applicable measures
  • Components
    • Number of disconnected sets
    • Strong: must be an arc in to all nodes
  • Community detection
    • See Mark Newman’s work (such as the Girvan-Newman algorithm)
  • Special K’s: K-shell, K-core, K-plex
world wide web k shells
World Wide Web: K-shells
  • http://xavier.informatics.indiana.edu/lanet-vi
community detection political blogs
Community Detection: Political Blogs
  • Adamic & Glance. 2004. The Political Blogosphere and the 2004 U.S. Election: Divided They Blog.
visualizing data
Visualizing Data
  • Applications
    • GUESS: great for tweaking based on attribute data. Technical, but powerful.
    • NetDraw: straightforward, integrates with UCInet
    • Pajek: fast, draws large networks, pretty
    • More coming out every week (See the work of Martin Wattenberg, Danyel Fisher and Fernanda Viegas)
  • Environments / Packages
    • JUNG, Prefuse, Piccolo, R (gplot)
visualization best practices
Visualization Best Practices

Most Important: Be Graph Literate.

Otherwise you’ll be impressed

with the first thing you draw,

regardless of its quality

  • General:
    • Do NOT show a graph for graph’s sake.
    • Huge networks often give cluttered pictures
    • ‘De-clutter’ by trimming to symmetric ties.
  • Drawing Nodes:
    • Size can often represent log(continuous variable).
    • Tint - can represent categorical or continuous variable.
    • Do not show ego in an egonet.
    • Only use labels on small graphs (n < 50).
  • Layout
    • Spring-embedder layouts work nicely.
    • Post-layout touch ups are possible using ‘bin packing’ (in GUESS).
example digg com
Example - Digg.com

Popular Stories

Stories from Friends

Today’s Top Stories

digg using networks to predict the news
Digg: Using networksto Predict the News
  • Data gathered in early March
  • All Digg Users with 7 or more top stories (subset of top 1000 Diggers) as of Feb 27
  • Mapped symmetric ties
    • Node size is log(# stories-6), brightness is degree.
  • Calculated number of ties (for links to top diggers & links to other diggers):
    • In to node: # Fans
    • Symmetric: # Friends
    • Out from nodes: # Watched
regression output predicting popular stories
Regression Output - Predicting # Popular Stories

Effect of fans

in high places

Very strong

models

online networks in context
Online networks in Context

Media Multiplexity:

There is a positive relationship between

the number of ways in which people connect

and tie strength (Haythornthwaite 1999)

networks in a pinch
Networks in a pinch
  • The number of ties is often the most significant.
  • Just ask.
  • Specify boundary conditions (e.g. people you have emailed in the past month)
  • Categories are help them remember and give you extra data points. (e.g. friends / workmates / relatives)
  • With a roster, you can get people to select from a list.
summary
Summary
  • Network analysis: Because sociology wasn’t nerdy enough already.
  • Involves a disparate suite of programs for capture, analysis and visualization.
  • Compelling visual imagery - maps of relationships.
  • Strong explanatory power in online spaces.
  • A host of meaningful metrics to choose from
  • Sometimes, the number of ties is enough.
many thanks
Many Thanks

Bernie Hogan

bernie.hogan@utoronto.ca

PhD Candidate, Department of Sociology

Research Coordinator, NetLab

Graduate Fellow, Knowledge Media Design Institute

University of Toronto

P.S. Ask me about my scripts and tools