Communities and the Networks that Define Them
That place where people, places, and things intersect is usually referred to as a "community." It doesn't matter what kind people, places, and things we're talking about, it's still a community, and social networks are simply the most recent incarnation. One thing disparate communities share, at least according to, Weixiong Zhang, a Washington University associate professor of computer science and engineering and of genetics, and Jianhua Ruan, a faculty member in Department of Computer Science at the University of Texas at San Antonio, are networks within communities that define the community's structure.
And to examine networks such as these, Zhang and Ruan have published in a paper entitled An Efficient Spectral Algorithm for Network Community Discovery and Its Applications to Biological and Social Networks an algorithm that automatically identify communities and their structures in various networks.
Many complex systems can be represented as networks, says Zhang, including the genetic networks, social networks, and the Internet itself. The community structure of networks features a natural division in which the vertices in each subnetwork are highly involved with each other, though connected less strongly with the rest of the network. Communities are relatively independent of one another structurally, but researchers think that each community may correspond to a fundamental functional unit. A community in a genetic network usually contains genes with similar functions, just as a community on the World Wide Web often corresponds to Web pages on similar topics.
Zhang and Ruan's algorithm is more scalable than existing similar algorithms and can detect communities at a finer scale and with a higher accuracy. One impact of having such a computational biology tool is found in the genomics field. Using this tool, researchers may be better able to identify and understand communities of genes and their networks as well as how they cooperate in causing diseases, such as sepsis, virus infections, cancer and Alzheimer's disease. Zhang and Ruan's algorithm is versatile enough that it has been applied to identify the community structure of a network of co-expressed genes involved in bacterial sepsis, whatever that is.
Like I said, a community is community, a network is a network, and an algorithm is the product of careful study.