Using ERGM (Exponential Random Graph Model) in Exploring Network Effects: A Case Study of Policy Networks

2019 ◽  
Vol 29 (1) ◽  
pp. 35-61
Author(s):  
Hyun Hee Park
Keyword(s):  
Random Graph ◽  
Network Effects ◽  
Graph Model ◽  
Policy Networks ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
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.

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.

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

2019 ◽  
Author(s):  
Jian Xie ◽  
Youyi Bi ◽  
Zhenghui Sha ◽  
Mingxian Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Random Graph ◽  
Market Competition ◽  
Network Modeling ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Graph Model ◽  
Exponential Random Graph Model ◽  
Random Graph Model ◽  
Exponential Random Graph ◽  
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 to modeling and analyzing the evolution of product competitions using multi-year product survey data. We adopt Separate Temporal Exponential Random Graph Model (STERGM) as the statistical inference framework because it considers the evolution of dynamic networks as two separate processes: formation and dissolution. This treatment allows designers to investigate why two products enter into competition and why a competitive relationship preserves or dissolves over time. In an open market, the available products to customers are continuously changing over the time, posing challenges for conventional modeling methods concerning fixed product input. Consequently, we propose to leverage “structural zeros” in STERGM to tackle the problem of modeling varying product competitors as nodes in dynamic networks. We use China’s automotive market as a case study to illustrate the implementation of the proposed approach and its benefits compared to the static network modeling approach based on Exponential Random Graph Model (ERGM). The results show that our approach identifies the driving factors associated with product attributes and current market competition structures for the change of competition in both formation and dissolution processes. The insights gained from this paper can help designers better interpret the temporal changes of product competition relations and make product design decisions with the aid of dynamic network-based models.

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