Mongiovì Misael, Di Natale Raffaele, Giugno Rosalba, Pulvirenti Alfredo, Ferro Alfredo, Sharan Roded
Dipartimento di Matematica ed Informatica, Università di Catania, V.le A. Doria, 6, Catania, 95125, Italy.
J Bioinform Comput Biol. 2010 Apr;8(2):199-218. doi: 10.1142/s021972001000477x.
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.
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。