通过对抗性学习和图表建模来实现多层次的社交网络对齐
Jingyuan Duan1, Zhao Kang1, Ling Tian2
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
概括
社交网络对齐通过解决平台差异和复杂关系,精确匹配不同平台的用户. 拟议的MAGSNA模型在指导和归因的社交网络中实现了卓越的准确性和稳定性.
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
- 网络分析 网络分析
背景情况:
- 社交网络对齐对于跨平台用户识别至关重要.
- 现有的方法与定向网络和平台差异作斗争.
- 在邻近分析中,子图异构构构成挑战.
研究的目的:
- 提出一种用于精确对准定向和归因社交网络的新方法.
- 为了解决平台差异和子图异构的挑战.
- 为了增强后续的跨网络应用程序.
主要方法:
- 开发了多层次的对抗性和基于图表的社交网络对齐 (MAGSNA).
- 采用随机步行和对抗网络的个人级别分析,用于拓和属性差异.
- 利用分区级别分析,使用图形轨道和对区分特征的中心意识精细化.
主要成果:
- 马格斯纳有效地统一网络,并学习歧视性特征.
- 该模型减轻了平台差异和子图异构.
- 与现实世界和合成数据集的最先进方法相比,实现了卓越的性能.
结论:
- MAGSNA提供精确和彻底的社交网络对齐.
- 该方法表现出具有竞争力的效率和卓越的稳定性.
- 为跨网络用户识别和应用程序提供了显著的进步.
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