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...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Jure Leskovec, Kevin J. Lang, Michael W. Mahoney
• Keywords: Approximation Algorithms, Internet Technology, Network Community, Objective Functions, WWW 2010