Strategies affecting Twitter-based networking pattern of South Korean politicians: social network analysis and exponential random graph model

2012 ◽  
Vol 48 (1) ◽  
pp. 409-423 ◽  
Author(s):  
Ho Young Yoon ◽  
Han Woo Park
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
South Korean ◽  
Exponential Random Graph

scholarly journals A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model

2019 ◽  
Vol 7 (1) ◽  
pp. 20-51 ◽  
Author(s):  
Philip Leifeld ◽  
Skyler J. Cranmer
Keyword(s):  
Network Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Real Data ◽  
The Other ◽  
Exponential Random Graph Model ◽  
Empirical Comparison ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Longitudinal Network Analysis

AbstractThe temporal exponential random graph model (TERGM) and the stochastic actor-oriented model (SAOM, e.g., SIENA) are popular models for longitudinal network analysis. We compare these models theoretically, via simulation, and through a real-data example in order to assess their relative strengths and weaknesses. Though we do not aim to make a general claim about either being superior to the other across all specifications, we highlight several theoretical differences the analyst might consider and find that with some specifications, the two models behave very similarly, while each model out-predicts the other one the more the specific assumptions of the respective model are met.

scholarly journals Forms of Dependence: Comparing SAOMs and ERGMs From Basic Principles

2016 ◽  
Vol 48 (1) ◽  
pp. 202-239 ◽  
Author(s):  
Per Block ◽  
Christoph Stadtfeld ◽  
Tom A. B. Snijders
Keyword(s):  
Social Network ◽  
Statistical Analysis ◽  
Random Graph ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Model Choice ◽  
Basic Principles ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Transition Level

Two approaches for the statistical analysis of social network generation are widely used; the tie-oriented exponential random graph model (ERGM) and the stochastic actor-oriented model (SAOM) or Siena model. While the choice for either model by empirical researchers often seems arbitrary, there are important differences between these models that current literature tends to miss. First, the ERGM is defined on the graph level, while the SAOM is defined on the transition level. This allows the SAOM to model asymmetric or one-sided tie transition dependence. Second, network statistics in the ERGM are defined globally but are nested in actors in the SAOM. Consequently, dependence assumptions in the SAOM are generally stronger than in the ERGM. Resulting from both, meso- and macro-level properties of networks that can be represented by either model differ substantively and analyzing the same network employing ERGMs and SAOMs can lead to distinct results. Guidelines for theoretically founded model choice are suggested.

scholarly journals A Case Study of Pyramid Scheme Finance Flow Network Based on Social Network Analysis

2019 ◽  
Vol 11 (16) ◽  
pp. 4370
Author(s):  
Feng ◽  
Sun ◽  
Gong
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Large Scale ◽  
Graph Model ◽  
Flow Network ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Pyramid Structure ◽  
Pyramid Scheme

(1) Background: The pyramid scheme has caused a large-scale plunder of finances due to the unsustainability of its operating model, which seriously jeopardizes economic development and seriously affects social stability. In various types of networks, the finance flow network plays an extremely important role in the pyramid scheme organization. Through the study of the finance network, the operational nature of pyramid scheme organizations can be effectively explored, and the understanding of pyramid scheme organizations can be deepened to provide a basis for dealing with them. (2) Methods: This paper uses the motifs analysis and exponential random graph model in social network analysis to study the micro-structure and the network construction process of the “5.03” pyramid scheme finance flow network in Hunan, China. (3) Results: The finance flow network is sparse, the microstructure shows a typical pyramid structure; finance flows within the community and eventually flows to the most critical personnel, there is no finance relationship between different communities, and there are few finance relationships between pyramid salesmen of the same level. The inductees are in a key position in the network, which may explain why they are transferred to prosecution.

Data-Driven Dynamic Network Modeling for Analyzing the Evolution of Product Competitions

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Jian Xie ◽  
Youyi Bi ◽  
Zhenghui Sha ◽  
Mingxian Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Survey Data ◽  
Random Graph ◽  
Network Modeling ◽  
Dynamic Network ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
Dynamic Network Modeling ◽  
The Impact

Abstract Understanding the impact of engineering design on product competitions is imperative for product designers to better address customer needs and develop more competitive products. In this paper, we propose a dynamic network-based approach for modeling and analyzing the evolution of product competitions using multi-year buyer survey data. The product co-consideration network, formed based on the likelihood of two products being co-considered from survey data, is treated as a proxy of products’ competition relations in a market. The separate temporal exponential random graph model (STERGM) is employed as the dynamic network modeling technique to model the evolution of network as two separate processes: link formation and link dissolution. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and the benefits of dynamic network models compared to the static network modeling approach based on an exponential random graph model (ERGM). The results show that since STERGM takes preexisting competition relations into account, it provides a pathway to gain insights into why a product may maintain or lose its competitiveness over time. These driving factors include both product attributes (e.g., fuel consumption) as well as current market structures (e.g., the centralization effect). With the proposed dynamic network-based approach, the insights gained from this paper can help designers better interpret the temporal changes of product competition relations to support product design decisions.

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