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邻居可感知图形神经网络用于关系异质的推特机器人检测
Yan Li1, Haoyu Lu2, Wanying Chen3
1WuXi University, Wuxi, China.
PloS one
|February 17, 2026
概括
恶意机器人威胁到在线平台. NeighborSense是一个新的框架,通过分析本地交互模式并适应网络结构以提高准确性来改善机器人检测.
科学领域:
- 人工智能的人工智能
- 网络安全 网络安全
- 社交网络分析 社交网络分析
背景情况:
- 恶意机器人破坏在线社交平台的完整性和安全性.
- 现有的图形神经网络 (GNN) 机器人检测方法难以适应局部特征分布和异质关系结构.
研究的目的:
- 为社交机器人检测开发一个适应性框架,克服当前基于GNN的方法的局限性.
- 通过利用关系图结构和节点特性来增强机器人检测.
主要方法:
- 开发了NeighborSense,这是一个使用邻里统计数据分析本地机器人-人类交互模式的框架.
- 基于社区统计数据创建了两个本地指标.
- 将指标集成到关系图卷积神经网络 (R-GCN) 中,通过用于门式聚合控制的动态捷径模块.
主要成果:
- 具有适应性门的R-GCN骨干实现了关系异质性,局部和局部特征异质性的意识.
- 与最先进的方法相比,NeighborSense在检测准确度方面取得了持续的改进.
结论:
- 通过结合自适应聚合机制,NeighborSense有效地解决了基于GNN的机器人检测的局限性.
- 拟议的框架为识别社交平台上的恶意机器人提供了更强大,更准确的解决方案.
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