稀疏真实感图中的社区检测:改进贝特黑森算法

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2019-05-12 DOI:10.1109/ICASSP.2019.8683594

Lorenzo Dall'Amico, Romain Couillet

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引用次数: 6

摘要

本文改进了最近提出的用于稀疏图社区检测的Bethe Hessian矩阵，假设这里有一个更现实的节点度是非齐次的设置。值得注意的是，在Bethe Hessian聚类的开创性工作中提出的参数化可以得到改善，并对正确的分类率产生积极的影响。大量的模拟支持了我们的说法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Community Detection in Sparse Realistic Graphs: Improving the Bethe Hessian

This article improves over the recently proposed Bethe Hessian matrix for community detection on sparse graphs, assuming here a more realistic setting where node degrees are inhomogeneous. We notably show that the parametrization proposed in the seminal work on the Bethe Hessian clustering can be ameliorated with positive consequences on correct classification rates. Extensive simulations support our claims.

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来源期刊

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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