scholarly journals Dynamic Evolution of Venture Capital Network in Clean Energy Industries Based on STERGM

2019 ◽  
Vol 11 (22) ◽  
pp. 6313
Author(s):  
Chen Zhang ◽  
Xinghua Dang ◽  
Tao Peng ◽  
Chaokai Xue
Keyword(s):  
Venture Capital ◽  
Exponential Family ◽  
Network Formation ◽  
Graph Model ◽  
Clean Energy ◽  
Energy Industry ◽  
Geographic Proximity ◽  
Random Graph Model ◽  
Structural Embeddedness ◽  
Governmental Venture Capital

This paper provides a detailed description of venture capital (VC) investments in clean energy industries in China over the period 2006–2017 and explores the evolution of clean energy industry VC networks through network formation and network dissolution. Results from the separable temporal exponential-family random graph model (STERGM) show that the factors vary in their relative importance for clean energy industry VC network formation and dissolution. Specifically, governmental venture capital (GVC) and geographic proximity have strong impacts on the formation of networks but not on their dissolution. Reputation and structural embeddedness promote the formation of networks and inhibit their dissolution, and cognitive proximity is found to cause network formation while facilitating network dissolution. The results provide practical and theoretical guidance for the network development of VC firms investing in clean energy industries.

scholarly journals Modeling Unobserved Heterogeneity in Social Networks with the Frailty Exponential Random Graph Model

2018 ◽  
Vol 26 (1) ◽  
pp. 3-19 ◽  
Author(s):  
Janet M. Box-Steffensmeier ◽  
Dino P. Christenson ◽  
Jason W. Morgan
Keyword(s):  
Random Graph ◽  
Network Formation ◽  
Unobserved Heterogeneity ◽  
Graph Model ◽  
Monte Carlo Analysis ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Model ◽  
Random Graph Models ◽  
Exponential Random Graph

In the study of social processes, the presence of unobserved heterogeneity is a regular concern. It should be particularly worrisome for the statistical analysis of networks, given the complex dependencies that shape network formation combined with the restrictive assumptions of related models. In this paper, we demonstrate the importance of explicitly accounting for unobserved heterogeneity in exponential random graph models (ERGM) with a Monte Carlo analysis and two applications that have played an important role in the networks literature. Overall, these analyses show that failing to account for unobserved heterogeneity can have a significant impact on inferences about network formation. The proposed frailty extension to the ERGM (FERGM) generally outperforms the ERGM in these cases, and does so by relatively large margins. Moreover, our novel multilevel estimation strategy has the advantage of avoiding the problem of degeneration that plagues the standard MCMC-MLE approach.

The configuration model

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Degree Sequence ◽  
Graph Model ◽  
Network Models ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Configuration Model ◽  
Bipartite Networks ◽  
Small Component ◽  
Random Graph Model ◽  
Average Size

A discussion of the most fundamental of network models, the configuration model, which is a random graph model of a network with a specified degree sequence. Following a definition of the model a number of basic properties are derived, including the probability of an edge, the expected number of multiedges, the excess degree distribution, the friendship paradox, and the clustering coefficient. This is followed by derivations of some more advanced properties including the condition for the existence of a giant component, the size of the giant component, the average size of a small component, and the expected diameter. Generating function methods for network models are also introduced and used to perform some more advanced calculations, such as the calculation of the distribution of the number of second neighbors of a node and the complete distribution of sizes of small components. The chapter ends with a brief discussion of extensions of the configuration model to directed networks, bipartite networks, networks with degree correlations, networks with high clustering, and networks with community structure, among other possibilities.

Random graphs

2018 ◽  
Author(s):  
Mark Newman
Keyword(s):  
Random Graph ◽  
Random Graphs ◽  
Large Scale ◽  
Random Network ◽  
Graph Model ◽  
Clustering Coefficient ◽  
Giant Component ◽  
Random Graph Model ◽  
Definition Of ◽  
Average Size

An introduction to the mathematics of the Poisson random graph, the simplest model of a random network. The chapter starts with a definition of the model, followed by derivations of basic properties like the mean degree, degree distribution, and clustering coefficient. This is followed with a detailed derivation of the large-scale structural properties of random graphs, including the position of the phase transition at which a giant component appears, the size of the giant component, the average size of the small components, and the expected diameter of the network. The chapter ends with a discussion of some of the shortcomings of the random graph model.

scholarly journals Symptom and Age Homophilies in SARS-CoV-2 Transmission Networks during the Early Phase of the Pandemic in Japan

Biology ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 499
Author(s):  
Ali Andalibi ◽  
Naoru Koizumi ◽  
Meng-Hao Li ◽  
Abu Bakkar Siddique
Keyword(s):  
Early Stage ◽  
Graph Model ◽  
Initial Response ◽  
Contact Tracing ◽  
Pcr Analysis ◽  
Exponential Random Graph Model ◽  
Transmission Networks ◽  
Random Graph Model ◽  
Healthcare Settings ◽  
Exponential Random Graph

Kanagawa and Hokkaido were affected by COVID-19 in the early stage of the pandemic. Japan’s initial response included contact tracing and PCR analysis on anyone who was suspected of having been exposed to SARS-CoV-2. In this retrospective study, we analyzed publicly available COVID-19 registry data from Kanagawa and Hokkaido (n = 4392). Exponential random graph model (ERGM) network analysis was performed to examine demographic and symptomological homophilies. Age, symptomatic, and asymptomatic status homophilies were seen in both prefectures. Symptom homophilies suggest that nuanced genetic differences in the virus may affect its epithelial cell type range and can result in the diversity of symptoms seen in individuals infected by SARS-CoV-2. Environmental variables such as temperature and humidity may also play a role in the overall pathogenesis of the virus. A higher level of asymptomatic transmission was observed in Kanagawa. Moreover, patients who contracted the virus through secondary or tertiary contacts were shown to be asymptomatic more frequently than those who contracted it from primary cases. Additionally, most of the transmissions stopped at the primary and secondary levels. As expected, significant viral transmission was seen in healthcare settings.

scholarly journals Recent advances on biomass-fueled microbial fuel cell

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jamile Mohammadi Moradian ◽  
Zhen Fang ◽  
Yang-Chun Yong
Keyword(s):  
Fuel Cell ◽  
Microbial Fuel Cell ◽  
Electric Energy ◽  
Clean Energy ◽  
Biomass Energy ◽  
Energy Resources ◽  
Energy Industry ◽  
Renewable Energy Resources ◽  
Future Perspectives ◽  
The Earth

AbstractBiomass is one of the most abundant renewable energy resources on the earth, which is also considered as one of the most promising alternatives to traditional fuel energy. In recent years, microbial fuel cell (MFC) which can directly convert the chemical energy from organic compounds into electric energy has been developed. By using MFC, biomass energy could be directly harvested with the form of electricity, the most convenient, wide-spread, and clean energy. Therefore, MFC was considered as another promising way to harness the sustainable energies in biomass and added new dimension to the biomass energy industry. In this review, the pretreatment methods for biomass towards electricity harvesting with MFC, and the microorganisms utilized in biomass-fueled MFC were summarized. Further, strategies for improving the performance of biomass-fueled MFC as well as future perspectives were highlighted.

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