论文标题
新颖的机器学习算法，用于YouTube社交网络中的中心性和集团检测
Novel Machine Learning Algorithms for Centrality and Cliques Detection in Youtube Social Networks
论文作者
论文摘要
该研究项目的目的是使用机器学习技术来分析社交网络的动态，以定位最大的集团并找到集群，以识别目标人群。无监督的机器学习技术在本项目中设计和实施，以分析YouTube的数据集，以发现社交网络中的社区并找到中心节点。实现了不同的聚类算法并将其应用于YouTube数据集。在这项研究中，有效地使用了著名的Bron-Kerbosch算法来找到最大的集团。从这项研究中获得的结果可用于广告目的和构建智能推荐系统。所有算法均使用Python编程语言实现。实验结果表明，我们能够通过集团中心和学位中心成功地找到中心节点。通过利用集团检测算法，研究表明了机器学习算法如何在较大网络中检测近距离组。
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
