Visual analysis for evaluation of community detection algorithms

Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.A