资源分类
》 资源 》论文》Network Global Testing by Counting Graphlets

Network Global Testing by Counting Graphlets

2020-03-20 | |  57 |   45 |   0

Abstract

Consider a large social network with possibly severe degree heterogeneity and mixedmemberships. We are interested in testing whether the network has only one community or there are more than one communities. The problem is known to be non-trivial, partially due to th presence of severe degree heterogeneity. We construct a class of test statistics using the numbers of short paths and short cycles, and the key to our approach is a general framework for canceling the effects of degree heterogeneity. The tests compare favorably with existing methods. We support our methods with careful analysis and numerical study with simulated data and a real data example.

上一篇：Improving Optimization for Models With Continuous Symmetry Breaking

下一篇：Scalable Bilinear ? Learning Using State and Action Features

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...