具有邻域重叠的复杂网络中关键种子传播者的混合识别框架

IF 2.3 3区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Journal of Intelligent Information Systems Pub Date : 2024-03-15 DOI:10.1007/s10844-024-00849-w
Tianchi Tong, Min Wang, Wenying Yuan, Qian Dong, Jinsheng Sun, Yuan Jiang
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引用次数: 0

摘要

识别复杂网络中的关键种子传播者是一个研究动态传播过程和分析网络性能的开放性课题。然而,大多数研究结果都是基于节点度(如 Kshell 分解)设计分层模型来获取全局信息,而识别各层权重值带来的影响则比较粗糙。此外,在分层结构中,邻近节点有时是不相连的,因此无法有效捕捉局部结构信息。为了解决这些问题,本文利用解释性结构模型设计了一种基于最短路径距离的新型分层结构,并确定了各层的影响权重。此外,我们还考虑了分层结构中连接邻域节点和非连接邻域节点两种情况,设计了局部邻域重叠系数和基于重叠的局部指数(LIO)。为了达到全面识别和精确找到关键种子传播者的目的,我们在新的混合识别框架中引入了影响权重向量、归一化后的局部评价指标矩阵和局部指标权重向量。该方法采用单调性关系、易感-易染-易感模型、互补累积分布函数、肯德尔系数、传播规模比和平均最短路径长度等一系列指标,在不同数据集中进行相应的实验和扩散能力评估。结果表明,我们的方法在识别效果和传播能力上都优于相关算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A hybrid recognition framework of crucial seed spreaders in complex networks with neighborhood overlap

Recognizing crucial seed spreaders of complex networks is an open issue that studies the dynamic spreading process and analyzes the performance of networks. However, most of the findings design the hierarchical model based on nodes’ degree such as Kshell decomposition for obtaining global information, and identifying effects brought by the weight value of each layer is coarse. In addition, local structural information fails to be effectively captured when neighborhood nodes are sometimes unconnected in the hierarchical structure. To solve these issues, in this paper, we design a novel hierarchical structure based on the shortest path distance by using the interpretative structure model and determine influence weights of each layer. Furthermore, we also design the local neighborhood overlap coefficient and the local index based on the overlap (LIO) by considering two conditions of connected and unconnected neighborhood nodes in the hierarchical structure. For reaching a comprehensive recognition and finding crucial seed spreaders precisely, we introduce influence weights vector, local evaluation index matrix after normalization and the weight vector of local indexes into a new hybrid recognition framework. The proposed method adopts a series of indicators, including the monotonicity relation, Susceptible-Infected-Susceptible model, complementary cumulative distribution function, Kendall’s coefficient, spreading scale ratio and average shortest path length, to execute corresponding experiments and evaluate the diffusion ability in different datasets. Results demonstrate that, our method outperforms involved algorithms in the recognition effects and spreading capability.

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来源期刊
Journal of Intelligent Information Systems
Journal of Intelligent Information Systems 工程技术-计算机:人工智能
CiteScore
7.20
自引率
11.80%
发文量
72
审稿时长
6-12 weeks
期刊介绍: The mission of the Journal of Intelligent Information Systems: Integrating Artifical Intelligence and Database Technologies is to foster and present research and development results focused on the integration of artificial intelligence and database technologies to create next generation information systems - Intelligent Information Systems. These new information systems embody knowledge that allows them to exhibit intelligent behavior, cooperate with users and other systems in problem solving, discovery, access, retrieval and manipulation of a wide variety of multimedia data and knowledge, and reason under uncertainty. Increasingly, knowledge-directed inference processes are being used to: discover knowledge from large data collections, provide cooperative support to users in complex query formulation and refinement, access, retrieve, store and manage large collections of multimedia data and knowledge, integrate information from multiple heterogeneous data and knowledge sources, and reason about information under uncertain conditions. Multimedia and hypermedia information systems now operate on a global scale over the Internet, and new tools and techniques are needed to manage these dynamic and evolving information spaces. The Journal of Intelligent Information Systems provides a forum wherein academics, researchers and practitioners may publish high-quality, original and state-of-the-art papers describing theoretical aspects, systems architectures, analysis and design tools and techniques, and implementation experiences in intelligent information systems. The categories of papers published by JIIS include: research papers, invited papters, meetings, workshop and conference annoucements and reports, survey and tutorial articles, and book reviews. Short articles describing open problems or their solutions are also welcome.
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