Decoding network communities

A new way of finding community structure within networks - anything from social networks such as Facebook, to transport networks, political voting networks, and protein interaction networks in biology - could help us understand how people are connected and how connections change over time. The new technique, developed by a team from the University of North Carolina, University of Oxford, and Harvard University, aims to be more realistic than conventional approaches, which only capture one type of connection or a network at only one moment in time. The new approach captures the totality of connections within a network and could be used to examine the different ways communities form; for example, analysing relationships between University students and staff across many different connections such as Facebook friendship, College affiliation, and subject studied. Alternatively, it could be used to track how one type of connection - such as Facebook friendship - changes over time. The technique is not limited to social networks as community detection has the potential to find important groups in many other applications, such as protein-protein interaction networks, transportation networks, and political voting networks. 'Capturing the complexity of people's relationships through networks such as Facebook and how these relationships change over time is a huge challenge,' said Dr Mason Porter of Oxford University's Mathematical Institute, an author of the report.