网络社区发现算法的实证比较
Empirical Comparison of Algorithms for Network Community Detection
摘要：
在大型现实世界的图，如大型社会或信息网络中检测集群或社区是一个相当感兴趣的问题。在实践中，人们通常选择一个目标函数，该目标函数捕获网络集群作为具有比外部连接性更好的内部连接性的节点集合的直觉，然后应用近似算法或近似算法来提取与目标函数相关并且“看起来像”感兴趣的应用的良好社区的节点集合。在本文中，我们探讨了一系列的网络社区发现方法，以比较它们，并了解它们的相对性能和系统偏差，他们确定的集群。我们评估几个常见的目标函数，用于正式的概念，网络社区，我们研究了几个不同的类的近似算法，旨在优化这些目标函数。此外，而不是简单地固定一个目标，并要求任何大小的最佳集群的近似值，我们考虑的优化问题的大小解决的版本。考虑社区质量作为其大小的函数，提供了一个更精细的镜头来检查社区发现算法，因为目标函数和近似算法通常具有不明显的大小依赖行为。
英文摘要：
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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