The purpose of this study was to investigate the structural variables influencing the formation and maintenance of the network in A Korean learning city. For this purpose, network data were collected from the 166 institutes participating lifelong learning network in A city. Social network analysis was used for analyzing the characteristics of network and structural variable, especially centrality, structural hole, and ERGM (Exponential Random Graph Model).
Through these analyses, the findings were as follows: First, the result showed that both centrality and structural hole were the characteristics of network in A learning city. Therefore, some of the institutes were leading nods and some of the institutes were connected indirectly. Second, the result showed that the coefficients reciprocity variable was statistically significant and more influential positively than other variables. Also transitivity, structural hole, and spreading pattern were statistically significant and influential positively. Third, popularity and arc were statistically significant and influential negatively. On the bases of these findings, suggestions were discussed.