After an emergency incident occurs, how to identify risks, predict trends and scientifically cope before the crisis erupts is the basic starting point of this study. In this study, a supernetwork model of the risk perception in emergencies is innovatively constructed from the perspective of the governance of risks. This supernetwork model includes three subnetworks: the similar relationship subnetwork that is composed of newly occurring emergencies, the chain relationship subnetwork that is composed of historical emergencies and the co-occurrence relationship subnetwork that is composed of the risk elements for emergencies. Afterwards, the feature similarity algorithm is applied to quantify the relations between newly occurring emergencies and historical emergencies, and then, the link prediction algorithm is applied to predict the risk elements that may be derived from the newly occurring emergencies. This will be beneficial to enhancing the scientific accuracy of decision-making by managers when coping with emergencies risks.
SlovicP. Perception of risk. Science1987; 236(4799): 280–285.
2.
ChoJLeeJ. An integrated model of risk and risk-reducing strategies. J Business Res2006; 59(1): 112–120.
3.
SlovicPFischhoffBLichtensteinS. Why study risk perception?Risk Anal1982; 2(2): 83–93.
4.
SjöbergL. Factors in risk perception. Risk Anal2000; 20(1): 1–12.
5.
AdemolaSAMusaASInnocentIO. Moderating effect of risk perception on financial knowledge, literacy and investment decision. Am Int J Econ Financ Res2019; 1(1): 34–44.
6.
PuSShaoZFangMet al. Spatial distribution of the public’s risk perception for air pollution: a nationwide study in China. Sci Total Environ2019; 655: 454–462.
7.
WangJLiu-LastresBRitchieBWet al. Risk reduction and adventure tourism safety: an extension of the risk perception attitude framework (RPAF). Tourism Manag2019; 74: 247–257.
8.
DryhurstSSchneiderCRKerrJet al. Risk perceptions of COVID-19 around the world. J Risk Res. Epub ahead of print 15April2020. DOI: 10.1080/13669877.2020.1758193.
9.
MaJLuoY. The classification of rumour standpoints in online social network based on combinatorial classifiers. J Inform Sci2019; 46(2): 191–204.
10.
GustafsodPE. Gender differences in risk perception: theoretical and methodological perspectives. Risk Anal1998; 18(6): 805–811.
11.
WachingerGRennOBeggCet al. The risk perception paradox – implications for governance and communication of natural hazards. Risk Anal2013; 33(6): 1049–1065.
12.
FrewerLJHowardCHedderleyDet al. Methodological approaches to assessing risk perceptions associated with food-related hazards. Risk Anal1998; 18(1): 95–102.
13.
FinucaneMLAlhakamiASlovicPet al. The affect heuristic in judgments of risks and benefits. J Behav Decis Mak2000; 13(1): 1–17.
14.
SugimotoANomuraSTsubokuraMet al. The relationship between media consumption and health-related anxieties after the Fukushima Daiichi nuclear disaster. PLoS ONE2013; 8(8): e65331.
15.
SiegristMCvetkovichG. Better negative than positive? Evidence of a bias for negative information about possible health dangers. Risk Anal2001; 21(1): 199–206.
16.
HahmSKnuthDKehlDet al. The impact of different natures of experience on risk perception regarding fire-related incidents: a comparison of firefighters and emergency survivors using cross-national data. Safe Sci2016; 82: 274–282.
17.
CarducciAFioreMAzaraAet al. Environment and health: risk perception and its determinants among Italian university students. Sci Total Environ2019; 691: 1162–1172.
18.
GerholdLWahlSDombrowskyWR. Risk perception and emergency food preparedness in Germany. Int J Disaster Risk Red2019; 37: 101183.
19.
WangLWatanabeT. Effects of environmental policy on public risk perceptions of haze in Tianjin city: a difference-in-differences analysis. Renew Sustain Energ Rev2019; 109: 199–212.
20.
BurnsWJSlovicP. Risk perception and behaviors: anticipating and responding to crises. Risk Anal2012; 32(4): 579–582.
21.
ZhangMWangXMannanMSet al. System dynamical simulation of risk perception for enterprise decision-maker in communication of chemical incident risks. J Loss Prev Proces Ind2017; 46: 115–125.
22.
QingYHuiminMAYanlingYU. Multi-agent risk identifier model of emergency management system engineering based on immunology. Syst Eng Proced2012; 4: 385–392.
23.
BarratABarabasiALCaldarelliGet al. Virtual round table on ten leading questions for network research. Europ Phys J B2004; 38(2): 143–145.
24.
NagurneyADongJ. Management of knowledge intensive systems as supernetworks: modeling, analysis, computations, and applications. Math Comput Model2005; 42(3–4): 397–417.
25.
XiYDangY. Method to analyze robustness of knowledge network based on weighted supernetwork model and its application. Syst Eng Theory Pract2007; 27(4): 134–140.
26.
FengZWangZChenY. The equilibrium of closed-loop supply chain supernetwork with time-dependent parameters. Transp Res Part E: Logis Transp Rev2014; 64: 1–11.
27.
YamadaTImaiKNakamuraTet al. A supply chain-transport supernetwork equilibrium model with the behaviour of freight carriers. Transp Res Part E: Logis Transp Rev2011; 47(6): 887–907.
28.
TianRYLiuYJ. Isolation, insertion, and reconstruction: three strategies to intervene in rumor spread based on supernetwork model. Decis Support Syst2014; 67: 121–130.
29.
MaNLiuY. SuperedgeRank algorithm and its application in identifying opinion leader of online public opinion supernetwork. Expert Syst Appl2014; 41(4): 1357–1368.
30.
ChiYTangXLianYet al. A supernetwork-based online post informative quality evaluation model. Knowl Based Syst2019; 168: 10–24.
31.
LiuYLiQTangXet al. Superedge prediction: what opinions will be mined based on an opinion supernetwork model?Decis Support Syst2014; 64: 118–129.
32.
GetoorLDiehlCP. Link mining: a survey. ACM Sigkdd Explor Newslet2005; 7(2): 3–12.
33.
KayaB. Hotel recommendation system by bipartite networks and link prediction. J Inform Sci2019; 46(1): 53–63.
34.
LüLZhouT. Link prediction in complex networks: a survey. Physica A2011; 390(6): 1150–1170.
35.
EsslimaniIBrunABoyerA. Densifying a behavioral recommender system by social networks link prediction methods. Soc Network Anal Min2011; 1(3): 159–172.
36.
LiLQianLChengJet al. Accurate similarity index based on the contributions of paths and end nodes for link prediction. J Inform Sci2015; 41(2): 167–177.
37.
YuCZhaoXAnLet al. Similarity-based link prediction in social networks: a path and node combined approach. J Inform Sci2017; 43(5): 683–695.
38.
PapadimitriouASymeonidisPManolopoulosY. Fast and accurate link prediction in social networking systems. J Syst Softw2012; 85(9): 2119–2132.
39.
FengXZhaoJCXuK. Link prediction in complex networks: a clustering perspective. Europ Phys J B2012; 85(1): 3.
40.
JaliliMOrouskhaniYAsgariMet al. Link prediction in multiplex online social networks. Royal Soc Open Sci2017; 4(2): 1–11.
41.
RealRVargasJM. The probabilistic basis of Jaccard’s index of similarity. Syst Biol1996; 45(3): 380–385.
42.
LeydesdorffL. On the normalization and visualization of author co-citation data: Salton’s Cosine versus the Jaccard index. J Am Soc Inform Sci Tech2008; 59(1): 77–85.
43.
ArroyoJMatéC. Forecasting histogram time series with k-nearest neighbours methods. Int J Forecast2009; 25(1): 192–207.
44.
KossinetsG. Effects of missing data in social networks. Social Netw2006; 28(3): 247–268.
45.
NewmanMEJ. Clustering and preferential attachment in growing networks. Phys Rev E2001; 64(2): 025102.
46.
ZhangQMLüLWangWQet al. Potential theory for directed networks. PLoS ONE2013; 8(2): e55437.
47.
MiloRShen-OrrSItzkovitzSet al. Network motifs: simple building blocks of complex networks. Science2002; 298(5594): 824–827.
48.
LüLZhouT. Link prediction. Beijing, China: Higher Education Press2013.
49.
HanleyJAMcNeilBJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology1982; 143(1): 29–36.
