ABSTRACT
This chapter discusses various techniques proposed in the literature for clustering networks/graphs. It presents a comprehensive survey on different approaches to network clustering and describes a wide range of clustering algorithms based on their working principles. The chapter discusses commonly used evaluation criteria followed by a detailed discussion and analysis of different graph clustering algorithms proposed in the literature. It examines different formulations of the problem in the literature and most commonly used evaluation criteria for measuring the quality of the resulting clusters. The chapter also describes core methods and representative algorithms in each category starting from early geometric partitioning through to modern spectral methods and Markov clustering. It explores major emerging challenges and state-of-the-art approaches to solving them in the area of network clustering. The chapter explains some of the modern research themes that will guide the advancement of future research in the area of network clustering in a broader sense.
