Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.
|Number of pages||12|
|Journal||Physica A: Statistical Mechanics and its Applications|
|Early online date||24 Nov 2017|
|Publication status||Published - 15 Feb 2018|
Bibliographical noteFunding Information:
This research was supported by the Hong Kong Research Grants Council under the GRF Grant CityU 11234916 and the National Natural Science Foundation of China under Grant No. 61473321.
© 2017 Elsevier B.V.
- Evolutionary game
- Multi-local-world networks
- Naming game
- Social community