Extracting a subset of representative users from the original set in social networks plays a critical role in Social Network Analysis. In existing studies, some researchers focus on preserving users’ characteristics when sampling representative users, while others pay attention to preserving the topology structure. However, both users’ characteristics and the network topology contain abundant information of users. Thus, it is critical to preserve both of them while extracting the representative user subset. To achieve the goal, we propose a novel approach in this study, and formulate the problem as RUS (Representative User Subset) problem that is proved as an NP-Hard problem. To solve RUS problem, we propose two approaches KS (K-Selected) and an optimized method (ACS) that are both consisted of a clustering algorithm and a sampling model, where a greedy heuristic algorithm is proposed to solve the sampling model. In addition, we propose the pruning strategy by taking advantage of MaxHeap structure. To validate the performance of the proposed approach, extensive experiments are conducted on two real-world datasets. Results demonstrate that our methods outperform state-of-the-art approaches.

中文翻译：

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针对用户特征和拓扑特征提取社交网络的代表性用户子集

从社交网络的原始集合中提取代表性用户的子集在社交网络分析中起着至关重要的作用。在现有研究中，一些研究人员在对代表性用户进行采样时专注于保留用户的特征，而另一些研究人员则关注保留拓扑结构。但是，用户的特征和网络拓扑都包含大量的用户信息。因此，在提取有代表性的用户子集的同时保留它们两者至关重要。为了实现该目标，我们在本研究中提出了一种新颖的方法，并将该问题表述为证明为NP-Hard问题的RUS（代表用户子集）问题。为了解决RUS问题，我们提出了两种方法KS（K选择）和优化方法（ACS），两者均由聚类算法和采样模型组成，提出了一种贪婪启发式算法来求解采样模型。另外，我们提出利用MaxHeap结构的修剪策略。为了验证所提出方法的性能，在两个真实世界的数据集上进行了广泛的实验。结果表明，我们的方法优于最新方法。