We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a ...

We study the asymptotics for sparse exponential random graph models where the parameters may depend on the number of vertices of the graph. We obtain exact estimates for the mean and variance of the ...

The growing availability of network data and of scientific interest in distributed systems has led to the rapid development of statistical models of network structure. Typically, however, these are ...

Abstract: Artificial society, which is a bottom-up method, has become a significant mean of studying complexity and complex phenomena in human society. Social networks play an important role in the ...

Venture capital firms use a variety of accumulated resources to inform their investment activities, but do the rely solely on their own resources or do they employ other firms' resources to complement ...

This study introduces a novel methodology for endogenous variable selection in Exponential Random Graph Models (ERGMs) to enhance the analysis of social networks across various scientific disciplines.

Abstract: Drug problem has contributed a rapid impact on today's society. It is not only a threat of human health but also causes a great impact on the social security issue. As drug abuse tends to ...

Applying the exponential random graph model (Robins et al. 2007) to the investment data of Japanese venture capital (VC) firms, we document the relationship between VC performance and the dynamics of ...