1. Measuring OSNs: Things Id Like to Know Nick Feamster Georgia Tech

2. 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

3. 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…

4. 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

5. 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.

6. 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?

7. 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?

8. 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?

9. 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