Collaborative Friendship Networks in Online Healthcare Communities: An Exponential Random Graph Model 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.

Download Full-text

Exponential Random Graph Model Fundamentals

Exponential Random Graph Models for Social Networks ◽

10.1017/cbo9780511894701.008 ◽

2013 ◽

pp. 49-76 ◽

Cited By ~ 3

Author(s):

Johan Koskinen ◽

Galina Daraganova

Keyword(s):

Random Graph ◽

Graph Model ◽

Exponential Random Graph Model ◽

Random Graph Model ◽

Exponential Random Graph

Download Full-text

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

Journal of Mechanical Design ◽

10.1115/1.4045687 ◽

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.

Download Full-text