Bluetooth (BT) data has been extensively used for recognizing social patterns and inferring social networks, as BT is widely present in everyday technological devices. However, even though collecting BT data is subject to random noise and may result in substantial measurement errors, there is an absence of rigorous procedures for validating the quality of the inferred BT social networks. This paper presents a methodology for inferring and validating BT-based social networks based on parameter optimization algorithm and social network analysis (SNA). The algorithm performs edge inference in a brute-force search over a given BT data set, for deriving optimal BT social networks by validating them with predefined ground truth (GT) networks. The algorithm seeks to optimize a set of parameters, predefined considering some reliability challenges associated to the BT technology itself. The outcomes show that optimizing the parameters can reduce the number of BT data false positives or generate BT networks with the minimum amount of BT data observations. The subsequent SNA shows that the inferred BT social networks are unable to reproduce some network characteristics present in the corresponding GT networks. Finally, the generalizability of the proposed methodology is demonstrated by applying the algorithm on external BT data sets, while obtaining comparable results.

蓝牙 (BT) 数据已广泛用于识别社交模式和推断社交网络，因为 BT 广泛存在于日常技术设备中。然而，尽管收集 BT 数据会受到随机噪声的影响，并可能导致重大测量误差，但仍缺乏严格的程序来验证推断的 BT 社交网络的质量。本文提出了一种基于参数优化算法和社交网络分析（SNA）来推断和验证基于 BT 的社交网络的方法。该算法在给定 BT 数据集上进行强力搜索时执行边缘推理，通过使用预定义的地面实况 (GT) 网络进行验证来导出最佳 BT 社交网络。该算法旨在优化一组参数，这些参数是考虑到与 BT 技术本身相关的一些可靠性挑战而预先定义的。结果表明，优化参数可以减少 BT 数据误报的数量，或者生成具有最少 BT 数据观测量的 BT 网络。随后的 SNA 显示，推断的 BT 社交网络无法重现相应 GT 网络中存在的一些网络特征。最后，通过将该算法应用于外部 BT 数据集，同时获得可比较的结果，证明了该方法的普遍性。