Detecting Communities Based on Network Topology
Communities are groups that are densely connected among their members, and sparsely connected with the rest of the network. Community structure can reveal abundant hidden information about complex networks that is not easy to detect by simple observation. There are many large-scale complex networks (systems) in the real world whose structure is not fully understood. A great deal of research has been carried out to uncover the structures of these real world networks, to improve the ability to manage, maintain, renovate and control them. With the help of varied approaches, it is possible to shed light on the general structure of these networks, and further understand their function.
Network science methods have been used in various settings1, 2, including social3, 4, information5, transportation6, energy7, ecological8, disease9, and biological networks10, 11, 12,13. In most of these cases we can find clear community structures, which are usually associated with specific functions. However, to date, most detection methods have limitations, and there is still a lot of room to develop more general approaches.
At present, most methods focus on the detection of node community. One popular approach is based on the optimization of the modularity Q14, 15, 52, 56 of a sub-network. Some methods13, 14,29, 34, 38, 39, 40 force every node to be assigned to a single community. This assumption doesn’t always reflect real world networks, where several overlapping communities can co-exist. For example, in social networks, a person may have family relationship circles, job circles, friend circles, social circles, hobby circles and so on. Algorithms that can discover overlapping communities16, 17, 18, 19, 20, 21, 22, 23 have been developed, and recently, methods to detect link communities20, 24, 25 have been presented. The concept of a link community is useful for discovering overlapping communities, as edges are more likely to have unique identities than nodes, which instead tend to have multiple identities. In addition, statistical54, information-theoretic35, 48, 53 and synchronization and dynamical clustering approaches49, 50, 58, 59, 60 have also been developed to detect communities.
A useful tool for policy making, because it helps identify communities and how they interact to form super-communities.
The essence of mapping the polity and the public, socially, economically, technologically, and infrastrucutrally.
Think about it.