k -双模拟:一种简化异构信息网络的新方法

IF 6.2 2区 计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS
Yongjie Liang , Wujie Hu , Jinzhao Wu
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引用次数: 0

摘要

异构信息网络(HINs)正变得越来越重要和广泛应用;然而,关注HINs分支结构的研究较少。基于并发系统和异构信息网络的共性,以及双仿真等价在并发系统中的重要应用,本文提出了属于同一节点类型的节点之间的k-双仿真,旨在简化其分支结构,从而得到一个性价比较高的模型,其中k为正积分,与节点的相似度密切相关。在本文中,我们初步定义了节点k-双仿真的概念。随后,我们提出了一种识别HINs中同类型节点间k-双模拟的计算方法。利用这种方法,我们可以得到一个近似于原网络的网络。理论和实践分析表明,原始网络和双元网络之间的连通路径误差是可控的。实验结果表明,与原始网络相比,得到的网络在保留相同或相似信息的情况下,节点和边的数量有所减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
K-bisimulation: A novel approach for simplifying heterogeneous information networks
Heterogeneous information networks (HINs) are becoming increasingly important and widely used; however, fewer studies are focusing on the branch structures within HINs. Based on the commonalities of concurrent systems and heterogeneous information networks, as well as the significant application of bisimulation equivalence in concurrent systems, this article proposes k-bisimulation among nodes belonging to same node type, aiming to simplify the branching structure of that to obtain a cost-effective model, wherein the k is a positive integrate being closely related to the similarity degree of nodes. In this paper, we initially define the notion of k-bisimulation for nodes. Subsequently, we propose a computational method to identify k-bisimulation among nodes of same type in HINs. With the assistance of this method, we can derive a network that is approximately bisimular to the original one. Theoretical and practical analysis reveals that errors in connected paths between the original and bisimular networks are controllable. Experimental results indicate that, in comparison to the original network, the obtained network exhibits a reduction in the number of nodes and edges, while still preserve same or similar information.
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来源期刊
CiteScore
19.90
自引率
2.70%
发文量
376
审稿时长
10.6 months
期刊介绍: Computing infrastructures and systems are constantly evolving, resulting in increasingly complex and collaborative scientific applications. To cope with these advancements, there is a growing need for collaborative tools that can effectively map, control, and execute these applications. Furthermore, with the explosion of Big Data, there is a requirement for innovative methods and infrastructures to collect, analyze, and derive meaningful insights from the vast amount of data generated. This necessitates the integration of computational and storage capabilities, databases, sensors, and human collaboration. Future Generation Computer Systems aims to pioneer advancements in distributed systems, collaborative environments, high-performance computing, and Big Data analytics. It strives to stay at the forefront of developments in grids, clouds, and the Internet of Things (IoT) to effectively address the challenges posed by these wide-area, fully distributed sensing and computing systems.
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