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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >Recursive Structure Similarity: A Novel Algorithm for Graph Clustering
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Recursive Structure Similarity: A Novel Algorithm for Graph Clustering

机译：递归结构相似性：图形聚类的新算法

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摘要

A various number of graph clustering algorithms have been proposed and applied in real-world applications such as network analysis, bio-informatics, social computing, and etc. However, existing algorithms usually focus on optimizing specified quality measures at the global network level, without carefully considering the destruction of local structures which could be informative and significant in practice. In this paper, we propose a novel clustering algorithm for undirected graphs based on a new structure similarity measure which is computed in a recursive procedure. Our method can provide robust and high-quality clustering results, while preserving informative local structures in the original graph. Rigorous experiments conducted on a variety of benchmark and protein datasets show that our algorithm consistently outperforms existing algorithms.
机译：已经提出了各种数量的图形聚类算法，并应用于现实世界应用，例如网络分析，生物信息学，社交计算等。然而，现有算法通常专注于在全球网络级别优化规定的质量措施，而无需仔细考虑在实践中破坏局部结构，这在实践中可能是有信息和重要的。在本文中，我们提出了一种基于在递归过程中计算的新结构相似度测量的多向图形的新型聚类算法。我们的方法可以提供强大而高质量的聚类结果，同时在原始图中保留信息丰富的本地结构。在各种基准和蛋白质数据集上进行的严格实验表明我们的算法始终如一地优于现有算法。

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