Threshold effects in networks trigger global cascades which may generate the failure of such systems. This phenomenon was observed in the case of iWiW, a very popular Hungarian online social network which collapsed due to the cascading abandon of the service by its users, driven by exogenous and endogenous factors. In this research, we analyze the dataset of iWiW and try to characterize some of the dynamical features of the networks, basing our study on the timestamped interactions between users. We first look at the degree distribution of the network and then focus on identifying some of the ego-centered networks. We will then detect the communities in each ego-centered network with the Louvain algorithm and analyze the rank correlation between the registration dates and the last login dates of users inside the communities, comparing the obtained coefficients with the expected rank correlations for a null model.