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Network Completion and Survey Sampling

This study investigates the challenge of network topology inference when vertex information is known. By employing random surveys asking individuals about their connections, we seek to bound the number of incorrect link predictions. Our outline includes notation, tight bounds, relaxations of these bounds, and a practical example. We explore the computational complexities and necessary learning biases for effective inference. This research indicates that with robust sampling techniques, confident learning of network structures can be achieved, potentially extending to methodologies like Snowball Sampling.

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Network Completion and Survey Sampling

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Presentation Transcript

  1. Network Completion and Survey Sampling Steve Hanneke and Eric P. Xing Machine Learning Department Carnegie Mellon University

  2. The Task I know the vertices, but what does the network look like? Infer what the rest of the network looks like. Send out some random surveys Survey:“Who are you linked to?” Want a bound on number of pairs with incorrect predictions.

  3. Outline • Notation • A Tight Bound • Relaxations of the bound • An Example

  4. Notation

  5. A Tight Bound

  6. A Tight Bound • The max in FT might be computationally hard (open problem) • Difficult to intuitively understand the behavior • So we’d like to relax the bound

  7. Relaxing the Bound

  8. Relaxing the Bound

  9. Relaxing the Bound

  10. Proof

  11. Example

  12. Example

  13. Conclusions • In theory, can confidently learn a network topology from survey samples • Need a fairly strong learning bias • Future: Extensions to Snowball Sampling?

  14. Thanks

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