Identification of influential spreaders based on classified neighbors in real-world complex networks

Li, Chao; Wang, Li; Sun, Shiwen; Xia, Chengyi

doi:10.1016/j.amc.2017.10.001

Cited by 81 publications

(43 citation statements)

References 56 publications

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“…The use of a network science perspective allows us to study complex system processes through the explicit representation of relations between actors, such as pathways or the interdependence of decision-making. In recent years, a vast body of literature has emerged on diffusion processes in complex networks [14,15], such as the spread of epidemics [16] or information [17]. We build on this research and extend it to the evaluation of policies that aim to promote a certain technology adoption behavior, explicitly taking into account the interdependencies between decision-makers and the complex social networks in which they are embedded.…”

Section: The Need For a Complexity Science Approach To Policymentioning

confidence: 99%

Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Brugger

Henry

2019

Complexity

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Agent-based models are used to explore how social networks influence the effectiveness of governmental programs to promote the adoption of solar photovoltaics (solar PV) by residential households. This paper examines how a common characteristic of social networks, known as network segregation, can dampen the indirect benefits of solar incentive programs that arise from peer effects. Peer effects cause an agent to be more likely to adopt a technology if they are socially connected to other adopters. Due to network segregation, programs that target relatively affluent agents can generate rapid increases in overall adoption levels but at the cost of increasing disparities in access to solar technology between rich and poor communities. These dynamics are explored through theoretical agent-based models of solar adoption within hypothetical social systems. The effectiveness of three types of solar incentive programs, the feed-in tariff, leasing programs, and seeding programs, is explored. Even though these programs promote rapid adoption in the short term, results demonstrate that network segregation can create serious distributional justice problems in the long term for some programs. The distributional justice effects are particularly severe with the feed-in tariff. Overall, this paper provides an illustration of how agent-based models may be used to evaluate and experiment with policy interventions in a virtual space, which enhances the scientific basis of policymaking.

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Section: The Need For a Complexity Science Approach To Policymentioning

confidence: 99%

Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Brugger

Henry

2019

Complexity

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“…In recent studies, researchers have uncovered that the characteristics of neighboring nodes (i.e. the semi-local information or its local structure) strongly influence the nodes’ spreading capability 32 , 35 , 36 . In addition, some studies have shown that when super-spreaders—as identified through local or semi-local measurements and metrics—belong to the same local community, their spreading effectiveness maybe high within that community but can be seriously hindered at the global network level.…”

Section: Introductionmentioning

confidence: 99%

Spatial super-spreaders and super-susceptibles in human movement networks

Chin

Bouffanais

2020

Sci Rep

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As lockdowns and stay-at-home orders start to be lifted across the globe, governments are struggling to establish effective and practical guidelines to reopen their economies. In dense urban environments with people returning to work and public transportation resuming full capacity, enforcing strict social distancing measures will be extremely challenging, if not practically impossible. Governments are thus paying close attention to particular locations that may become the next cluster of disease spreading. Indeed, certain places, like some people, can be “super-spreaders”. Is a bustling train station in a central business district more or less susceptible and vulnerable as compared to teeming bus interchanges in the suburbs? Here, we propose a quantitative and systematic framework to identify spatial super-spreaders and the novel concept of super-susceptibles, i.e. respectively, places most likely to contribute to disease spread or to people contracting it. Our proposed data-analytic framework is based on the daily-aggregated ridership data of public transport in Singapore. By constructing the directed and weighted human movement networks and integrating human flow intensity with two neighborhood diversity metrics, we are able to pinpoint super-spreader and super-susceptible locations. Our results reveal that most super-spreaders are also super-susceptibles and that counterintuitively, busy peripheral bus interchanges are riskier places than crowded central train stations. Our analysis is based on data from Singapore, but can be readily adapted and extended for any other major urban center. It therefore serves as a useful framework for devising targeted and cost-effective preventive measures for urban planning and epidemiological preparedness.

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“…With the development of economic globalization, the financial system has become more and more closely interconnected by investment networks, debtor–creditor and trade contacts [ 1 , 2 , 3 , 4 ]. Financial institutions such as depositories, broker-dealers and insurance companies permeate each other by related business and display significant complex network properties [ 5 , 6 , 7 ]. The failure of several financial institutions may lead to a severe economic crisis [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

Xin

Zhao

Gong

et al. 2020

Entropy

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The investigation of the systemic importance of financial institutions (SIFIs) has become a hot topic in the field of financial risk management. By making full use of 5-min high-frequency data, and with the help of the method of entropy weight technique for order preference by similarities to ideal solution (TOPSIS), this paper builds jump volatility spillover network of China’s financial institutions to measure the SIFIs. We find that: (i) state-owned depositories and large insurers display SIFIs according to the score of entropy weight TOPSIS; (ii) total connectedness of financial institution networks reveal that Industrial Bank, Ping An Bank and Pacific Securities play an important role when financial market is under pressure, especially during the subprime crisis, the European sovereign debt crisis and China’s stock market disaster; (iii) an interesting finding shows that some small financial institutions are also SIFIs during the financial crisis and cannot be ignored.

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Identification of influential spreaders based on classified neighbors in real-world complex networks

Cited by 81 publications

References 56 publications

Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Equity of Incentives: Agent-Based Explorations of How Social Networks Influence the Efficacy of Programs to Promote Solar Adoption

Spatial super-spreaders and super-susceptibles in human movement networks

Systemic Importance of China’s Financial Institutions: A Jump Volatility Spillover Network Review

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