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Graph Sampling Approach for Reducing Computational Complexity of Large-Scale Social Network
arXiv - CS - Social and Information Networks Pub Date : 2021-02-17 , DOI: arxiv-2102.08881
Andry Alamsyah, Yahya Peranginangin, Intan Muchtadi-Alamsyah, Budi Rahardjo, Kuspriyanto

Online social network services provide a platform for human social interactions. Nowadays, many kinds of online interactions generate large-scale social network data. Network analysis helps to mine knowledge and pattern from the relationship between actors inside the network. This approach is important to support predictions and the decision-making process in many real-world applications. The social network analysis methodology, which borrows approaches from graph theory provides several metrics that enabled us to measure specific properties of the networks. Some of the metrics calculations were built with no scalability in minds, thus it is computationally expensive. In this paper, we propose a graph sampling approach to reduce social network size, thus reducing computation operations. The performance comparison between natural graph sampling strategies using edge random sampling, node random sampling, and random walks are presented on each selected graph property. We found that the performance of graph sampling strategies depends on graph properties measured.

中文翻译:

降低大规模社交网络计算复杂度的图抽样方法

在线社交网络服务为人类社交互动提供了平台。如今,许多类型的在线互动都产生了大规模的社交网络数据。网络分析有助于从网络内部参与者之间的关系中挖掘知识和模式。这种方法对于支持许多实际应用中的预测和决策过程很重要。社交网络分析方法学借鉴了图论的方法,它提供了多种指标,使我们能够测量网络的特定属性。某些度量标准计算在构建时就没有考虑可扩展性,因此计算量很大。在本文中,我们提出了一种图抽样方法来减少社交网络的规模,从而减少计算操作。在每个选定的图属性上,介绍了使用边缘随机采样,节点随机采样和随机游动的自然图采样策略之间的性能比较。我们发现图采样策略的性能取决于所测量的图属性。
更新日期:2021-02-18
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