Temporal Graph Reproduction With RWIG

IF 6.7 2区计算机科学 Q1 ENGINEERING, MULTIDISCIPLINARY

IEEE Transactions on Network Science and Engineering Pub Date : 2025-03-31 DOI:10.1109/TNSE.2025.3555974

Sergey Shvydun;Anton-David Almasan;Piet Van Mieghem

引用次数: 0

Abstract

We examine the Random Walkers Induced temporal Graph (RWIG) model, which generates temporal graphs based on the co-location principle of

$M$

independent walkers that traverse the underlying Markov graph with different transition probabilities. Given the assumption that each random walker is in the steady state, we determine the steady-state vector

$\tilde{s}$

and the Markov transition matrix

$P_{i}$

of each walker

$w_{i}$

that can reproduce the observed temporal network

$G_{0},{{\ldots }},G_{K\text{--}1}$

with the lowest mean squared error. We also examine the performance of RWIG for periodic temporal graph sequences.

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用RWIG再现时间图

我们研究了随机步行者诱导时间图（RWIG）模型，该模型基于$M$独立步行者的共定位原理生成时间图，这些步行者以不同的转移概率遍历底层马尔可夫图。假设每个随机步行者都处于稳态，我们确定了每个步行者的稳态向量$\tilde{s}$和马尔可夫转移矩阵$P_{i}$，它可以以最小的均方误差再现观测到的时间网络$G_{0},{{\ldots}},G_{K\text{—}1}$。我们还研究了RWIG对周期时间图序列的性能。

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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来源期刊

IEEE Transactions on Network Science and Engineering Engineering-Control and Systems Engineering

CiteScore

12.60

自引率

9.10%

发文量

393

期刊介绍： The proposed journal, called the IEEE Transactions on Network Science and Engineering (TNSE), is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level. The types of networks covered include physical or engineered networks, information networks, biological networks, semantic networks, economic networks, social networks, and ecological networks. Aimed at discovering common principles that govern network structures, network functionalities and behaviors of networks, the journal seeks articles on understanding, prediction, and control of structures and behaviors of networks. Another trans-disciplinary focus of the IEEE Transactions on Network Science and Engineering is the interactions between and co-evolution of different genres of networks.