The Network Characteristic of Chinese SMEs in Malaysia and Their Performance

2015 ◽  
pp. 39-47 ◽  
Author(s):  
Law Kuan Kheng ◽  
Mohd Sobri Minai
Keyword(s):  
Network Characteristic

Friends in High Places

2014 ◽  
Vol 6 (3) ◽  
pp. 63-91 ◽  
Author(s):  
Lauren Cohen ◽  
Christopher J. Malloy
Keyword(s):  
Voting Behavior ◽  
State Level ◽  
Network Characteristic ◽  
Personal Connections ◽  
Vote Trading

We demonstrate that personal connections amongst US politicians have a significant impact on Senate voting behavior. Networks based on alumni connections between politicians are consistent predictors of voting behavior. We estimate sharp measures that control for common characteristics of the network, as well as heterogeneous impacts of a common network characteristic across votes. We find that the effect of alumni networks is close to 60 percent as large as the effect of state-level considerations. We show that politicians use school ties as a mechanism to engage in vote trading (“logrolling”), and that alumni networks help facilitate the procurement of discretionary earmarks. (JEL D72, D85, Z13)

Optimized Manipulation of the Network Characteristic Modes for Wideband Small Antenna Matching

2017 ◽  
Vol 65 (11) ◽  
pp. 5757-5767 ◽  
Author(s):  
Hussein Jaafar ◽  
Sylvain Collardey ◽  
Ala Sharaiha
Keyword(s):  
Network Characteristic ◽  
Characteristic Modes ◽  
Small Antenna

scholarly journals Changes of Spatiotemporal Pattern and Network Characteristic in Population Flow under COVID-19 Epidemic

2021 ◽  
Vol 10 (3) ◽  
pp. 145
Author(s):  
Chengming Li ◽  
Zheng Wu ◽  
Lining Zhu ◽  
Li Liu ◽  
Chengcheng Zhang
Keyword(s):  
Local Governments ◽  
Spatiotemporal Pattern ◽  
Specific Information ◽  
Peak Period ◽  
Network Characteristic ◽  
Outbreak Period ◽  
Serial Connection ◽  
Epidemic Period ◽  
The Difference ◽  
The Impact

The COVID-19 pandemic is a major problem facing humanity throughout the world. The rapid and accurate tracking of population flows may therefore be epidemiologically informative. This paper adopts a massive amount of daily population flow data (from January 10 to March 15, 2020) for China obtained from the Baidu Migration platform to analyze the changes of the spatiotemporal patterns and network characteristics in population flow during the pre-outbreak period, outbreak period, and post-peak period. The results show that (1) for temporal characteristics of population flow, the total population flow varies greatly between the three periods, with an overall trend of the pre-outbreak period flow > the post-peak period flow > the outbreak period flow. Impacted by the lockdown measures, the population flow in various provinces plunged drastically and remained low until the post-peak period, at which time it gradually increased. (2) For the spatial pattern, the pattern of population flow is divided by the geographic demarcation line known as the Hu (Heihe-Tengchong) Line, with a high-density interconnected network in the southeast half and a low-density serial-connection network in the northwest half. During the outbreak period, Wuhan city appeared as a hollow region in the population flow network; during the post-peak period, the population flow increased gradually, but it was mainly focused on intra-provincial flow. (3) For the network characteristic changes, during the outbreak period, the gap in the network status between cities at different administrative levels narrowed significantly. Thus, the feasibility of Baidu migration data, comparison with non-epidemic periods, and optimal implications are discussed. This paper mainly described the difference and specific information under non-normal situation compared with existing results under a normal situation, and analyzed the impact mechanism, which can provide a reference for local governments to make policy recommendations for economic recovery in the future under the epidemic period.

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