Influential Nodes in the OBOR Fossil Energy Trade Network Based on D-S Theory: Detection and Evolution Analysis

Table 1

List of fossil energy trade network studies in recent years.

Authors (year)	Modeling	Type of network	Category	Scope and time	Evaluation indexes
Peng et al. (2021) [9]		Transportation	LNG	World, 2013–2017	DC, L, CC, C,
Bu et al. (2020) [10]		Consumption	Gas	China, 2005–2017	DC, BC, CC, LMDI
Wang and Li (2019) [11]		Transportation	Coal	China, 1997–2016	DC, BC,L, CC,
Wang et al. (2019) [12]		Trade	Coal	World, 1996–2015	DC, BC, C
Xi et al. (2019) [13]		Trade	Oil	OBOR, 2009–2016	DC, BC, CC
An et al. (2018) [14]		Trade	Oil	World, 2014–2017	PMI, C
Guan and An (2017) [15]		Trade	Oil, coal, gas, PV	World, 2014	BC, ND, RankS, LP
Zhong et al. (2017) [16]		Trade	Coal, oil, gas	World, 2000–2013	C, NMI
Gao et al. (2015) [17]		Trade	Coal, oil, gas	World, 2002–2013	DC, C, NMI
Ji et al. (2014) [18]		Trade	Oil	World, 2010	EI, DC, CC, C, NMI
Zhong et al. (2014) [19]		Trade	Oil	World, 2002–2011	C, NMI
An et al. (2014) [20]		Trade	Oil	World, 1993–2012	DC, CC, C, stability

Note. DC: degree centrality, L: shortest path length, CC: closeness centrality, C: community structure, BC: betweenness centrality, NMI: normalized mutual information, LMDI: logarithmic mean Divisia index, PMI: pointwise mutual information, ND: network density, RankS: ranking score, LP: link prediction, and EI: export intensity.