.png){kind=icon}
返回
Community Detection Based on Genetic Algorithm Using Local Structural Similarity
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2019.2939864.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Community detection is an important research direction in complex network analysis that aims to detect the community structure in networks via clustering operation, which has an important application value and practical significance in mining potential network information. Genetic algorithm (GA) is commonly used in community detection to solve NP-hard problems caused by modularity optimization effectively. However, in terms of GA, the problems of global search performance and slow convergence still remain due to the low accuracy of initial population generation and the randomness of the mutation operator. Thus, a novel local structural similarity-based GA (LSSGA) is proposed in this study to address such problems. It mainly contains two innovations: First, a new generation strategy for initial population based on local structural similarity and roulette wheel selection is designed to improve the quality of initial individuals, at the same time, maintain the diversity of the initial population. Secondly, an effective mutation operator based on label propagation and local structural similarity is proposed, which can quickly achieve targeted and effective mutation. The performance of LSSGA has been verified in many detailed aspects such as the convergence of initialization strategy, the effectiveness of overcome falling into local optimum, and rationality of objective function. Experimental results of both synthetic and real-world networks demonstrate the effective performance of the proposed method via systematic comparison with other existing advanced algorithms.
Keyword:
Heuristic algorithms
Partitioning algorithms
Genetic algorithms
Clustering algorithms
Classification algorithms
Optimization
Sociology
Complex network
community detection
genetic algorithm
local structural similarity
roulette wheel selection
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
From Louvain to Leiden: guaranteeing well-connected communities从鲁汶到莱顿: 保证良好连接的社区.png)
SCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
OAAI分享收藏分享收藏A multi-task neural network for multilingual sentiment classification and language detection on Twitter用于Twitter多语言情感分类和语言检测的多任务神经网络
PREAI
分享收藏
查看更多内容