多步贪心算法识别真实世界和计算机生成网络中的社团结构
Multistep greedy algorithm identifies community structure in real-world and computer-generated networks
摘要：
我们最近介绍了一个多步扩展的贪心算法的模块化优化。扩展是基于这样的想法，即在每次迭代时合并l对社区（l>1），防止过早地凝聚成几个大社区。在这里，提出了一个经验公式的步长l的选择，产生分区（接近）最佳模块化的17个现实世界和1100计算机生成的网络。此外，深入分析了两个真实世界网络的社区（细菌E。coli和MartinKarplus合著的论文标题中的共现词图）提供了证据，证明由多步贪心算法获得的划分不仅在模块性方面而且根据客观标准优于由原始贪心算法生成的划分。换句话说，贪心算法的多步扩展减少了陷入模块性局部最优的危险，并生成更合理的划分。
英文摘要：
We have recently introduced a multistep extension of the greedy algorithm for modularity optimization. The extension is based on the idea that merging l pairs of communities (l>1) at each iteration prevents premature condensation into few large communities. Here, an empirical formula is presented for the choice of the step width l that generates partitions with (close to) optimal modularity for 17 real-world and 1100 computer-generated networks. Furthermore, an in-depth analysis of the communities of two real-world networks (the metabolic network of the bacterium E. coli and the graph of coappearing words in the titles of papers coauthored by Martin Karplus) provides evidence that the partition obtained by the multistep greedy algorithm is superior to the one generated by the original greedy algorithm not only with respect to modularity but also according to objective criteria. In other words, the multistep extension of the greedy algorithm reduces the danger of getting trapped in local optima of modularity and generates more reasonable partitions.
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