概括
这项研究引入了一种基于图形的新型统计算法,用于检测数字市场中机器人和欺诈性用户等异常. 它在准确性和计算效率上都优于图形嵌入,增强了市场保护.
科学领域:
- 计算机科学 计算机科学
- 数据科学数据科学数据科学
- 网络安全 网络安全
背景情况:
- 数字市场面临异常活动的威胁,包括机器人和欺诈性用户.
- 现有的异常检测方法往往缺乏计算效率和可扩展性.
- 信息不对称和市场参与者的不充分保护是持续存在的挑战.
研究的目的:
- 评估和比较各种基于图形的方法来检测数字市场的异常.
- 提出一种高效,强大的威胁检测算法,以改善市场参与者的保护.
- 通过增强异常检测来减少信息不对称.
主要方法:
- 利用了三个公开可访问的基于图形的数据集:TwiBot-20 (Twitter社交网络),比特币OTC和比特币Alpha (加密货币市场).
- 与不同的图形算法进行比较,重点是节点统计与最先进的图形嵌入用于特征提取.
- 开发并提出了一个新的基于图形的统计算法用于异常检测.
主要成果:
- 基于图形的数据在预测异常方面明显优于文字数据.
- 与图形嵌入相比,基于节点统计的方法显示出优越的预测性能和计算效率.
- 基于节点统计的拟议算法提供了一个比图形嵌入更高效和更少的开销密集型解决方案,图形嵌入是数据集特定的,需要广泛的调整.
结论:
- 基于图表的统计方法在数字市场的异常检测方面非常有效,比图表嵌入提供更好的性能和效率.
- 新提出的算法为威胁检测提供了可扩展,强大和计算效率高的解决方案.
- 这些发现倡导基于节点统计的方法来增强数字市场的安全性和减少信息不对称.
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