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Creators/Authors contains: "Yufan Huang"

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  1. ( , International Conference on Machine Learning)
    We study a new connection between a technical measure called $\mu$-conductance that arises in the study of Markov chains for sampling convex bodies and the network community profile that characterizes size-resolved properties of clusters and communities in social and information networks. The idea of $\mu$-conductance is similar to the traditional graph conductance, but disregards sets with small volume. We derive a sequence of optimization problems including a low-rank semi-definite program from which we can derive a lower bound on the optimal $\mu$-conductance value. These ideas give the first theoretically sound bound on the behavior of the network community profile for a wide range of cluster sizes. The algorithm scales up to graphs with hundreds of thousands of nodes and we demonstrate how our framework validates the predicted structures of real-world graphs. 
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    Accepted Manuscript Full Text Available
  2. ( , International Conference on Machine Learning)
    Accepted Manuscript Full Text Available
  3. ; ; ( , Proceedings of the 40th International Conference on Machine Learning)
    Accepted Manuscript Full Text Available