Efficient Stochastic Local Search for Modularity Maximization

被引：0
|
作者：
Santiago, Rafael [1 ]
Lamb, Luis C. [2 ]
机构：
[1] Univ Vale Itajai, Itajai, Brazil
[2] Univ Fed Rio Grande do Sul, Porto Alegre, RS, Brazil
来源：
PROCEEDINGS OF THE 2016 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE (GECCO'16 COMPANION) | 2016年
关键词：
Clustering; Modularity Maximization; Stochastic Local Search;
D O I：
10.1145/2908961.2909003
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper, we analyze stochastic local searches and neighbor-hood strategies for the Modularity Maximization problem. Modularity Maximization was shown relevant in the identification of clusters in complex and social networks. Our experimental analysis shows that 1-neighborhood is a better strategy for the problem as it quickly locates suboptimal partitions. We find 99% near best-known partitions with 94% of frequency in time vertical bar V vertical bar(1:44) and 95% near best known Q values in sublinear time.
引用
收藏
页码：51 / 52
页数：2
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