NVSA:一种具有可变节点值的查询图搜索算法

NVSA: A Query Graph Search Algorithm with Variable Node Value

图模型作为一种重要的数据结构,常被应用于众多不同领域并被广泛研究。随着图数据规模的日益增大,大图上的子图搜索问题变得极为重要。然而,目前已有的研究成果在大图上的执行效率并不太理想,而且没有考虑查询图上存在节点值可变的情况。为解决具有可变节点值的查询图在大图上的搜索问题,本文提出基于双索引的NVSA算法。首先通过合并相邻同类点构建CP索引和Vin索引,然后根据索引结构优化加速子图搜索算法。真实数据集上的实验表明,NVSA算法具有有效性和高效性。

As an important data structure, graph model is often applied to many different fields and is widely studied. With the increasing size of graph data, the problem of subgraph search becomes very important. However, the existing research results are not very efficient on the large graph, and did not consider the existence of variable node value on the query graph. In order to solve the problem of search graph with variable node value on large graphs, this paper proposes an NVSA algorithm based on double indices. First, the CP index and Vin index are constructed by merging adjacent congeners, and then the subgraph search algorithm is accelerated according to the index structure. Experiments on real datasets show that the NVSA algorithm is efficient and efficient.