Post on 27-Mar-2015
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