Guanrong Chen

Biographic Data

ID4418024
NAMEGuanrong Chen
GIVEN NAMESGuanrong
FAMILY NAMEChen
SIGNATURECHEN G
AFFILIATIONSCity University of Hong Kong
ORCID0000-0003-1381-7418
VERIFIEDYes
TOTAL WORKS11
TOTAL CITATIONS3
WORKS AS AUTHOR10
WORKS AS EDITOR1
FIRST PUBLICATION YEAR1999
LATEST PUBLICATION YEAR2026
H-INDEX1

  • From Telettrofono to Smartphone

    Open AccessR Meucci, Guanrong ChenARTICLEIEEE Technology and Society…2026

    The origins of voice communication technologies are typically narrated through a sequence of industrial and scientific milestones culminating in today’s global digital networks. Yet, early developments in telephony also reveal deeply human motivations and socially embedded forms of innovation. This article compares Antonio Meucci’s nineteenth-century “telettrofono”—a low-energy, household communication device conceived to assist his ailing wife—w…

  • Characterizing regional importance in cities with human mobility motifs in metro networks

    Open AccessShuyang Shi, Ding Lyu et al.ARTICLEJournal of Transport Geography2025References: 5

  • Understanding the structural evolution and driving mechanisms of urban network using firm-level big data and TERGM modeling

    Open AccessShuju Hu, Guanrong Chen et al.ARTICLECities2025Cited by: 3References: 2

  • Mutual Influence in Citation and Cooperation Patterns

    Open AccessChenbo Fu, Haogeng Luo et al.ARTICLEIEEE Transactions on Computational…2024

    Measuring the influence of scientists and their activities on science and society is important and indeed essential for many studies. Despite the substantial efforts devoted to exploring the influence’s measures and patterns of an individual scientific enterprise, it remains unclear how to quantify the mutual impact of multiple scientific activities. This work quantifies the relationship between the scientists’ interactive activities and their in…

  • DeepInsight

    Open AccessJunhao Zhu, Jinhuan Wang et al.ARTICLEIEEE Transactions on Computational…2024

    With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks (GNNs), have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This article investigates three well-known …

  • Enhancing Controllability Robustness of q-Snapback Networks through Redirecting Edges

    Open AccessYang Lou, Lin Wang et al.ARTICLEResearch2019

    The well-known small-world network model was established by randomly rewiring edges, aiming to enhance the synchronizability of an undirected nearest-neighbor regular network. This paper demonstrates via extensive numerical simulations that randomly redirecting edges could enhance the robustness of the network controllability for directed snapback networks against both random and intentional node-removal and edge-removal attacks

  • Evolutionary Algorithms, Swarm Dynamics and Complex Networks

    Open AccessIvan Zelinka, Guanrong ChenEDITORBOOKEvolutionary Algorithms, Swarm…2017

  • Propagation Dynamics on Complex Networks

    Open AccessXinchu Fu, Meredith Small et al.BOOKPropagation Dynamics on Complex…2014

  • Chaos, CNN, Memristors and Beyond

    Andrew Adamatzky, Guanrong Chen et al.BOOKChaos, CNN, Memristors and Beyond2013

  • Chaos, CNN, Memristors and Beyond

    Andrew Adamatzky, Eleonora Bilotta et al.BOOKChaos, CNN, Memristors and Beyond2013

  • Kalman Filtering

    Open AccessCharles K Chui, Guanrong Chen et al.BOOKKalman Filtering1999