Measuring OSNs:Things I’d Like to Know

Nick FeamsterGeorgia Tech

Why Measure Social Networks?

• Trustworthy Applications– Secure Channels [Authenticatr, Lockr]– Spam filters and whitelists [Re:, LineUp]– Automated backup systems [Friendstore]– Anti-censorship [Anti-Blocker]

• Advertising and Relationship Management

• Real-world Social Networking– Real-world socializing [Serendipity, aka-aki]– Public health applications

What We Need to Know

• Structure: Where are links/nodes in the graph?

• Semantics: What does a “link” imply?

• Visibility: Are there unknown links?

• Dynamics: How do graphs evolve?

• Invariants: (How) do OSNs differ?Sounds familiar…

Structure

• Problem: Where are links/edges in the graph?– Application specific metrics are more interesting than

high-level properties

• Example #1: Anti-censorship– Want to find the existence of “rings” in the social

network topology– The graph structure will determine what we can use

for a “deniable” clickstream

• Example #2: Collaborative measurement– Graph structure determines vantage points/nework

graph that each user has

Semantics

• Problem: In a social network, what determines weight/trust?– Frequency of communication– Type of communication– Common interests

• Some other graphs: the semantics are more clear because there is a notion of “weight”

• Links may not directly reflect network behavior– What are the sources/catalysts for link formation?– Getting Closer or Drifting Apart? Mobius et al.

Visibility

• Problem: How complete are graph measurements?

• Many social networks prevent “scraping”

• Aspects of profile are restricted/not public– May make it difficult to see some “links”– This sounds familiar, too: Analogous to hidden

peering links in AS graph?

Dynamics

• Serendipity Project– Real-world interactions create links in social graph– New OSN links create interactions in the real world

• Challenges:– Understanding graph evolution may rely on exogenous

factors that are difficult to measure

Real-world Interactions

Evolution of OSN Graph

• Problem: How does the network evolve over time?

Invariants

• What constitutes a “representative” data set?– Graph properties may vary by application (PGP

keys, email, Facebook, YouTube, etc.)

• Suppose that you are an advertiser, application builder, etc.– What conclusions can be drawn from a

measurement study on one social network?

Can We Avoid Repeating Mistakes?

• Separation of exogenous factors

• Explanatory/evocative models– Exploration of why certain links form– Impact on applications

• Closing the loop– Effects on real-world behavior