document: Empirical comparison of algorithms for network community detection Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest . In practice , one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity , and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that `` look like '' good communities for the application of interest . In this paper , we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify . We evaluate several common objective functions that are used to formalize the notion of a network community , and we examine several different classes of approximation algorithms that aim to optimize such objective functions . In addition , rather than simply fixing an objective and asking for an approximation to the best cluster of any size , we consider a size-resolved version of the optimization problem . Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms , since objective functions and approximation algorithms often have non-obvious size-dependent behavior .
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