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Efficient and Exact Local Search for Random Walk Based Top-K Proximity Query in Large Graphs
IEEE Transactions on Knowledge and Data Engineering ( IF 8.9 ) Pub Date : 2016-05-01 , DOI: 10.1109/tkde.2016.2515579
Yubao Wu 1 , Ruoming Jin 2 , Xiang Zhang 1
Affiliation  

Top-$k$ proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective, efficiently computing them on large graphs is a challenging task. In this paper, we develop an efficient and exact local search method, FLoS (Fast Local Search), for top-$k$ proximity query in large graphs. FLoS guarantees the exactness of the solution. Moreover, it can be applied to a variety of commonly used proximity measures. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several operations to manipulate transition probabilities and develop tight lower and upper bounds on the proximity values. The lower and upper bounds monotonically converge to the exact proximity value when more nodes are visited. We further extend FLoS to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments on real and synthetic large graphs to evaluate the efficiency and effectiveness of the proposed method.

中文翻译:

大图中基于随机游走的 Top-K 邻近查询的高效精确局部搜索

最佳-$千$大图中的邻近查询是一个具有广泛应用的基本问题。已经提出了各种基于随机游走的措施来测量不同节点之间的接近度。尽管这些措施是有效的,但在大图上有效地计算它们是一项具有挑战性的任务。在本文中,我们开发了一种高效且精确的局部搜索方法 FLoS(快速局部搜索),用于顶部搜索$千$大图中的邻近查询。FLoS 保证了解决方案的准确性。此外,它可以应用于各种常用的邻近度量。FLoS 基于没有局部最优邻近度量的属性。我们表明许多措施没有局部最优。利用这个特性,我们引入了几种操作来操纵转移概率并制定接近值的严格下限和上限。当访问更多节点时,下限和上限单调收敛到精确的接近度值。我们通过利用不同度量之间的关系将 FLoS 进一步扩展到具有局部最优的度量。我们对真实和合成的大图进行了综合实验,以评估所提出方法的效率和有效性。
更新日期:2016-05-01
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