Suppr超能文献

SIGMA:一种基于集合覆盖的不精确图匹配算法。

SIGMA: a set-cover-based inexact graph matching algorithm.

作者信息

Mongiovì Misael, Di Natale Raffaele, Giugno Rosalba, Pulvirenti Alfredo, Ferro Alfredo, Sharan Roded

机构信息

Dipartimento di Matematica ed Informatica, Università di Catania, V.le A. Doria, 6, Catania, 95125, Italy.

出版信息

J Bioinform Comput Biol. 2010 Apr;8(2):199-218. doi: 10.1142/s021972001000477x.

Abstract

Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching sub-networks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the set-cover problem that concerns overlapping multi-sets. We extensively test our method and compare it to a baseline method and to the state-of-the-art Grafil. We show that SIGMA outperforms both, providing higher pruning power in all the tested scenarios.

摘要

网络查询是一个不断发展的领域,有着广泛的应用,从针对已知分子数据库筛选化合物到跨物种匹配子网络。图索引是一种用于搜索大型图数据库的强大方法。迄今为止,大多数图索引方法都处理精确匹配(同构)问题,这限制了它们在存在此类匹配的特定实例中的适用性。在此,我们提供一种新颖的图索引方法来处理更一般的不精确匹配问题。我们的方法SIGMA基于对涉及重叠多重集的集合覆盖问题的一个变体进行近似。我们对我们的方法进行了广泛测试,并将其与基线方法以及当前最先进的Grafil进行比较。我们表明SIGMA在所有测试场景中均优于这两者,具有更高的剪枝能力。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验