Beckett Stephen J
Biosciences, College of Life and Environmental Sciences , University of Exeter , Exeter EX4 4QE, UK.
R Soc Open Sci. 2016 Jan 20;3(1):140536. doi: 10.1098/rsos.140536. eCollection 2016 Jan.
Real-world complex networks are composed of non-random quantitative interactions. Identifying communities of nodes that tend to interact more with each other than the network as a whole is a key research focus across multiple disciplines, yet many community detection algorithms only use information about the presence or absence of interactions between nodes. Weighted modularity is a potential method for evaluating the quality of community partitions in quantitative networks. In this framework, the optimal community partition of a network can be found by searching for the partition that maximizes modularity. Attempting to find the partition that maximizes modularity is a computationally hard problem requiring the use of algorithms. QuanBiMo is an algorithm that has been proposed to maximize weighted modularity in bipartite networks. This paper introduces two new algorithms, LPAwb+ and DIRTLPAwb+, for maximizing weighted modularity in bipartite networks. LPAwb+ and DIRTLPAwb+ robustly identify partitions with high modularity scores. DIRTLPAwb+ consistently matched or outperformed QuanBiMo, while the speed of LPAwb+ makes it an attractive choice for detecting the modularity of larger networks. Searching for modules using weighted data (rather than binary data) provides a different and potentially insightful method for evaluating network partitions.
现实世界中的复杂网络由非随机的定量相互作用组成。识别那些彼此之间的相互作用比整个网络中节点间相互作用更为频繁的节点群落，是多个学科的关键研究重点，然而许多群落检测算法仅使用节点间相互作用存在与否的信息。加权模块度是评估定量网络中群落划分质量的一种潜在方法。在此框架下，通过搜索使模块度最大化的划分，可以找到网络的最优群落划分。试图找到使模块度最大化的划分是一个计算难题，需要使用算法。QuanBiMo是一种为使二分网络中的加权模块度最大化而提出的算法。本文介绍了两种新算法，LPAwb+和DIRTLPAwb+，用于使二分网络中的加权模块度最大化。LPAwb+和DIRTLPAwb+能够稳健地识别出具有高模块度分数的划分。DIRTLPAwb+始终与QuanBiMo匹配或表现更优，而LPAwb+的速度使其成为检测更大网络模块度的一个有吸引力的选择。使用加权数据（而非二元数据）搜索模块，为评估网络划分提供了一种不同且可能具有深刻见解的方法。
