Motif analysis of urban rail transit network

Ma, Yunfang; Sallán, José M.; Lordan, Oriol

doi:10.1016/j.physa.2023.129016

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…Motifs, which are locally small connected subgraphs, play a significant role in the formation of networks. Ma et al pointed out that the p-value measures the probability that the frequency of a motif structure in the random network is higher than that in the real network; at the same time, the Z-value represents the ratio of the difference between the number of occurrences of a motif structure in the real network and that in the random network to the standard deviation of the number of occurrences of the motif structure in the random network [42]. Therefore, a smaller p-value means that the motif structure is more common in the real network, and a p-value of 1 indicates that the motif structure is invalid; a higher Z-value indicates that the motif structure is more important in the real network, and a non-positive Z-value indicates that the motif structure is invalid.…”

Section: Motif Structure Analysismentioning

confidence: 99%

Analysis of the Driving Mechanism of Urban Carbon Emission Correlation Network in Shandong Province Based on TERGM

Song,

Xiao,

Liu

2024

Sustainability

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Analyzing the driving factors and mechanisms of urban carbon emission correlation networks can provide effective carbon reduction decision-making support for Shandong Province and other regions with similar industrial characteristics. Based on industrial carbon emission data from various cities in Shandong Province from 2013 to 2021, the spatial correlation network of carbon emission was established by using a modified gravity model. The characteristics of the network were explored by using the Social Network Analysis (SNA) method, and significant factors affecting the network were identified through Quadratic Assignment Procedure (QAP) correlation analysis and motif analysis. The driving mechanism of the carbon emission correlation network was analyzed by using Temporal Exponential Random Graph Models (TERGMs). The results show that: (1) The spatial correlation network of urban carbon emission in Shandong Province exhibits multi-threaded complex network correlations with a relatively stable structure, overcoming geographical distance limitations. (2) Qingdao, Jinan, and Rizhao have high degree centrality, betweenness centrality, and closeness centrality in the network, with Qingdao and Jinan being relatively central. (3) Shandong Province can be spatially clustered into four regions, each with distinct roles, displaying a certain “neighboring clustering” phenomenon. (4) Endogenous network structures such as Mutual, Ctriple, and Gwesp significantly impact the formation and evolution of the network, while Twopath does not show the expected impact; FDI can promote the generation of carbon emission reception relationships in the spatial correlation network; IR can promote the generation of carbon emission spillover relationships in the spatial correlation network; GS, differences in GDP, differences in EI, and similarities of IR can promote the generation of organic correlations within the network; on the temporal level, the spatial correlation network of urban carbon emission in Shandong Province has shown significant stability during the study period.

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Section: Motif Structure Analysismentioning

confidence: 99%