A Briefing Survey on Advances of Coupled Networks With Various Patterns

Dong, Gaogao; Duan, Dongli; Xia, Yongxiang

doi:10.3389/fphy.2021.795279

Cited by 3 publications

(1 citation statement)

References 69 publications

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“…In recent years, the rapid development of big data technology has provided powerful support for obtaining complex relational data among cities [12,13], thus becoming a new perspective and method for measuring the spatial structure of cities and regions [14]. However, previous studies have analyzed the socioeconomic patterns of PURs and their polycentric structures using enterprise networks or transportation networks [15], while ignoring the crucial functions of PURs as innovation incubators and hinges, especially the lack of analysis on the evolution process of knowledge polycentricity in PURs [11].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network

Tang

Dou

2022

Front. Phys.

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Polycentric city-regional development has become a buzzword for urban scholars and planners around the world. Inspired by the two critical characteristics of the polycentric urban region (PUR) as incubators and hinges of knowledge production, this study revealed the evolution of the polycentric structure and driving mechanism of PUR from the perspective of innovation network by using unique inter-city patent cooperation data. The results show that: 1) The innovation networks exhibit hierarchical and uneven characteristics, with the economic core cities becoming incubators and hinges of innovation and dominating the development of innovation networks. 2) There is compatibility between morphological polycentricity and functional polycentricity in PURs, but there are also differences. Although the morphological polycentricity of three PURs has improved, functional polycentricity is underdeveloped. 3) The geographic, institutional, institutional, and cultural proximity play the facilitating role for inter-city knowledge flows and their polycentric enhancement, but city attributes such as population size have a lower impact. Our study is believed to renew the knowledge polycentricity conceptualization and enlighten the evolution of innovation network.

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Section: Introductionmentioning

confidence: 99%

Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network

Tang

Dou

2022

Front. Phys.

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Network resilience of non-hub nodes failure under memory and non-memory based attacks with limited information

Dong

Wang

et al. 2022

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Previous studies on network robustness mainly concentrated on hub node failures with fully known network structure information. However, hub nodes are often well protected and not accessible to damage or malfunction in a real-world networked system. In addition, one can only gain insight into limited network connectivity knowledge due to large-scale properties and dynamic changes of the network itself. In particular, two different aggression patterns are present in a network attack: memory based attack, in which failed nodes are not attacked again, or non-memory based attack; that is, nodes can be repeatedly attacked. Inspired by these motivations, we propose an attack pattern with and without memory based on randomly choosing [Formula: see text] non-hub nodes with known connectivity information. We use a network system with the Poisson and power-law degree distribution to study the network robustness after applying two failure strategies of non-hub nodes. Additionally, the critical threshold [Formula: see text] and the size of the giant component [Formula: see text] are determined for a network configuration model with an arbitrary degree distribution. The results indicate that the system undergoes a continuous second-order phase transition subject to the above attack strategies. We find that [Formula: see text] gradually tends to be stable after increasing rapidly with [Formula: see text]. Moreover, the failure of non-hub nodes with a higher degree is more destructive to the network system and makes it more vulnerable. Furthermore, from comparing the attack strategies with and without memory, the results highlight that the system shows better robustness under a non-memory based attack relative to memory based attacks for [Formula: see text]. Attacks with memory can block the system’s connectivity more efficiently, which has potential applications in real-world systems. Our model sheds light on network resilience under memory and non-memory based attacks with limited information attacks and provides valuable insights into designing robust real-world systems.

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Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

Chen,

Zhang

et al. 2023

ACM Comput. Surv.

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Deep learning’s performance has been extensively recognized recently. Graph neural networks (GNNs) are designed to deal with graph-structural data that classical deep learning does not easily manage. Since most GNNs were created using distinct theories, direct comparisons are impossible. Prior research has primarily concentrated on categorizing existing models, with little attention paid to their intrinsic connections. The purpose of this study is to establish a unified framework that integrates GNNs based on spectral graph and approximation theory. The framework incorporates a strong integration between spatial- and spectral-based GNNs while tightly associating approaches that exist within each respective domain.

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A Briefing Survey on Advances of Coupled Networks With Various Patterns

Cited by 3 publications

References 69 publications

Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network

Exploring the Polycentric Structure and Driving Mechanism of Urban Regions From the Perspective of Innovation Network

Network resilience of non-hub nodes failure under memory and non-memory based attacks with limited information

Bridging the Gap between Spatial and Spectral Domains: A Unified Framework for Graph Neural Networks

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