Naik Debadatta, Dharavath Ramesh, Qi Lianyong
Indian Institute of Technology (ISM), Dhanbad, India.
China University of Petroleum (East China), Dongying, China.
Cluster Comput. 2023 Apr 13:1-19. doi: 10.1007/s10586-023-03993-0.
Unsupervised cluster detection in social network analysis involves grouping social actors into distinct groups, each distinct from the others. Users in the clusters are semantically very similar to those in the same cluster and dissimilar to those in different clusters. Social network clustering reveals a wide range of useful information about users and has many applications in daily life. Various approaches are developed to find social network users' clusters, using only links or attributes and links. This work proposes a method for detecting social network users' clusters based solely on their attributes. In this case, users' attributes are considered categorical values. The most popular clustering algorithm used for categorical data is the K-mode algorithm. However, it may suffer from local optimum due to its random initialization of centroids. To overcome this issue, this manuscript proposes a methodology named the Quantum PSO approach based on user similarity maximization. In the proposed approach, firstly, dimensionality reduction is conducted by performing the relevant attribute set selection followed by redundant attribute removal. Secondly, the QPSO technique is used to maximize the similarity score between users to get clusters. Three different similarity measures are used separately to perform the dimensionality reduction and similarity maximization processes. Experiments are conducted on two popular social network datasets; ego-Twitter, and ego-Facebook. The results show that the proposed approach performs better clustering results in terms of three different performance metrics than K-Mode and K-Mean algorithms.
社交网络分析中的无监督聚类检测涉及将社会行为者分组到不同的组中，每个组都与其他组不同。聚类中的用户在语义上与同一聚类中的用户非常相似，而与不同聚类中的用户不同。社交网络聚类揭示了关于用户的广泛有用信息，并且在日常生活中有许多应用。已经开发了各种方法来查找社交网络用户的聚类，这些方法仅使用链接或属性以及链接。这项工作提出了一种仅基于用户属性来检测社交网络用户聚类的方法。在这种情况下，用户属性被视为分类值。用于分类数据的最流行聚类算法是K-模式算法。然而，由于其质心的随机初始化，它可能会陷入局部最优。为了克服这个问题，本文提出了一种基于用户相似度最大化的名为量子粒子群优化方法的方法。在所提出的方法中，首先，通过执行相关属性集选择然后去除冗余属性来进行降维。其次，使用量子粒子群优化技术来最大化用户之间的相似度得分以获得聚类。分别使用三种不同的相似度度量来执行降维和相似度最大化过程。在两个流行的社交网络数据集上进行了实验；自我推特和自我脸书。结果表明，与K-模式和K-均值算法相比，所提出的方法在三种不同的性能指标方面表现出更好的聚类结果。
