A general growth model for online emerging user-object bipartite networks

The last couple of years have witnessed a huge surge in usage of the Internet due to the availability of several interactive applications of Web 2.0. Growth in both web users and varieties of online objects has been observed. In this paper, we propose a growth model for online emerging user-object bipartite networks to investigate selection behaviors of web users. According to model, both sets of users and objects grow constantly but edges arrive only from user set. The network evolves by arrival of external edges brought by new users and/or internal edges from old users. These external and internal edges are attached with a combination of preferential and random attachment mechanism. We have considered different attachment procedures for external edges of new users and internal edges of old users. To validate our model, we have taken nine different real online networks with distinct objects. Our results show good agreement between empirical data and theoretical model. We report mean absolute deviation (MAD), mean square error (MSE) and root mean square error (RMSE) for the proposed growth model. We emphasize on significant inferences about selection behaviors of web users after interpreting parameters' values of the model. We also report detailed analysis of randomness in selection of new and old users that gives interesting inferences for each of the considered online networks. (C) 2018 Published by Elsevier B.V.