Graph clustering has become a central tool for the analysis of networks in general, with manifold applications e.g. in data mining, social networks, biology or complex systems. The general aim of graph clustering is to identify dense groups in networks. Countless formalizations thereof exist, among those the widespread measure modularity. However, the overwhelming majority of algorithms for graph clustering relies on heuristics, e.g., for some NP-hard optimization problem, and do not allow for any structural guarantee on their output. Moreover, networks in the real world are often large, evolve over time or come as a data stream. The talk will discuss algorithmic aspects of graph clustering, especially quality measures and algorithms that are based on the intuition of identifying as clusters dense subgraphs that are loosely connected among one another. We will focus on the algorithm engineering methodology which consists in a cycle of design, analysis, implementation, and experimental evaluation of algorithms, bridging the gap between algorithm theory and practical applications. Special emphasis will be on clustering large networks.