Dynamic Social Networks Generator Based on Modularity: DSNG-M

Abstract

Continuous change is one of the key features of social networks, and the analysis and mining of dynamic social networks are of significant value. However, it is not easy to obtain real-world dynamic social networks. Thus, the artificial generation of dynamic social networks is very valuable. The dynamic social network generators that exist thus far usually generate social networks with specific operations, such as edge/node add/delete and community merge/split. In this paper, we describe the design of a dynamic social network generator based on modularity, called DSNG-M. DSNG-M initially takes a static social network and by flipping edges generates time-evolving social networks with the expected modularity, where the expected modularity at each time step is calculated based on the community structure of the original static social network. Thus, the generated networks and the original network have a common intrinsic structure, while the connections between nodes vary in the evolutionary process. We conducted experiments to analyze the change in the network characteristics of the generated social networks, such as the number of edges, degrees of nodes, and average distances between nodes. Experiments were also conducted to verify that the aggregation of multi-temporal social networks can reflect the community structure of the original social network and to analyze the effects of the generator's parameter on the time cost.