title: Competition between intra-community and inter-community synchronization and relevance in brain cortical networks.
abstract: In this paper the effects of inter-community links on the synchronization performance of community networks, especially on the competition between individual community and the whole network, are studied in detail. The study is organized from two aspects: the number or portion of inter-community links and the connection strategy of inter-community links between different communities. A critical point is found in the competition of global network and individual communities. Increasing the number of inter-community links will enhance the global synchronizability but degrade the synchronization performance of individual community before this point. After that the individual community will synchronize better and better as part of the whole network because the community structure is not so prominent. The critical point represents a balance region where the individual community is maximally independent while the information transmission remains effective between different communities. Among various connection strategies, connecting nodes belonging to different communities randomly rather than connecting nodes with larger degrees are the most efficient way to enhance global synchronization of the network. However, the dynamical modularity is the reverse case. A preferential connection scheme linking most of the hubs from the communities will allow rather efficient global synchronization while maintaining strong dynamical clustering of the communities. Interestingly, the observations are found to be relevant in a realistic network of cat cortex. The synchronization state is just at the critical point, which shows a reasonable combination of segregated function in individual communities and coordination among them. Our work sheds light on principles underlying the emergence of modular architectures in real network systems and provides guidance for the manipulation of synchronization in community networks.
authors.affiliation: Department of Physics, National University of Singapore, Singapore. zhaom17@gmail.com
authors.forename: Ming
authors.initials: M
authors.lastname: Zhao
authors.forename: Changsong
authors.initials: C
authors.lastname: Zhou
authors.forename: Jinhu
authors.initials: J
authors.lastname: Lü
authors.forename: Choy Heng
authors.initials: CH
authors.lastname: Lai
mesh_terms: D000818:Animals; D002415:Cats; D002540:Cerebral Cortex / Q000502:physiology*; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*