Social networks have long been a part of social science research and analysis. The internet, however, has greatly advanced network approaches to social science research. In particular, the internet itself is one enormous network, and understanding networks on the internet poses a significant methodological challenge due to its size and scale. One approach to analysing the internet as a network is to examine the network connectivity between websites. Hyperlinks between websites can be conceptualised as constructing a directed and weighted network; websites are nodes and the hyperlinks are edges. Such hyperlinks act as references from one site to another and are constructed for multiple purposes. A typical research objective in analysing such large networks is to identify the different sub-networks or communities that are structurally defined by more closely related connections. In recent years a range of algorithms have been developed to identify such sub-networks (or communities) within a larger network. This paper reviews a variety of such algorithms, and illustrates the community detection methodology using an example of a large-scale hyperlink network of over 19,000 websites and over 135,000 hyperlinks derived from 75 key UK government seed sites. Website communities were derived by using Infomap, a state-of-the-art community detection algorithm that operates on the principle that flows of information in complex networks reveals community structure. Identifying and analysing online communities in which government websites reside provides insights into how hyperlink communities are arranged, that is, their emergent organizing principals and the importance of government in these online communities. The example of government websites illustrates how community detection algorithms within larger networks can be fruitfully deployed to provide heretofore hidden insights to important social science problems, particularly in the burgeoning area of internet studies and web social science, and in the age of big data.