Thursday, May 11, 2017 - 3:00pm to 4:00pm
Location:Blelloch-Skees Conference Room 8115 Gates Hillman Centers
Speaker:DHIVYA ESWARAN, Ph.D. Student http://www.cs.cmu.edu/~deswaran/
Motivated by these observations, we formalize axioms that any node classification algorithm should obey and propose NetConf which satisfies these axioms and handles arbitrary network effects (homophily/heterophily) at scale. Our contributions are: (1) Axioms: We state axioms that any node classification algorithm should satisfy; (2) Theory: NetConf is grounded in a Bayesian-theoretic framework to model uncertainties, has a closed-form solution and comes with precise convergence guarantees; (3) Practice: Our method is easy to implement and scales linearly with the number of edges in the graph. On experiments using real world data, we always match or outperform BP while taking less processing time. Presented in Partial Fulfillment of the CSD Speaking Skills Requirement.