CliquePercolation: An R Package for conducting and visualizing results of the clique percolation network community detection algorithm.

Modeling complex phenomena as networks constitutes one of the – if not the most – versatile field of research (Barabási, 2011). Indeed, many interconnected entities can be represented as networks, in which entities are called nodes and their connections are called edges. For instance, networks can represent friendships between people, hyperlinks between web pages, or correlations between questionnaire items. One structural characteristic of networks that is investigated frequently across various sciences is the detection of communities (Fortunato, 2010). Communities are strongly connected subgraphs in the network such as groups of friends, thematic fields, or latent factors. Most community detection algorithms thereby put each node in only one community. However, nodes are often shared by multiple communities, e.g., when a person is part of multiple groups of friends, web pages belong to different thematic fields, or items load on multiple factors. The most popular community detection algorithm that is aimed at identifying such overlapping communities is the clique percolation algorithm (Farkas et al., 2007; Palla et al., 2005).