在动态网络上进行对比的表示学习
Pengfei Jiao1, Hongjiang Chen2, Huijun Tang2
1School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, China; Data Security Governance Zhejiang Engineering Research Center, Hangzhou, 310018, China.
概括
本研究介绍了一种新的动态网络对比表示学习 (DNCL) 模型,以改善动态网络的表示学习. 在稀疏或杂的网络中,DNCL通过专注于时间演变而不是仅仅是快照细节来提高稳健性.
科学领域:
- 机器学习 机器学习
- 网络科学 网络科学
- 数据挖掘 数据挖掘
背景情况:
- 动态网络表示学习旨在捕捉时间网络结构和节点属性.
- 现有的方法往往过度强调静态快照细节,导致稀疏或杂数据的性能差.
- 需要更强大的方法来有效地解释时间进化.
研究的目的:
- 为动态网络提出一个新的对比学习框架,命名为动态网络对比表示学习 (DNCL).
- 提高节点嵌入在动态网络中的稳定性和准确性,特别是在稀疏或杂的条件下.
- 为了捕捉内部快照拓和内部快照时间演变信息.
主要方法:
- 开发了一个动态网络对比表示学习 (DNCL) 模型,利用对比学习原理.
- 基于快照内和快照间对比的构建对比对象函数.
- 节点之间在不同的时间步骤和生成的视图之间最大限度地交换信息,避免直接估计基本真相特征.
主要成果:
- 与最先进的方法相比,DNCL在链接预测,节点分类和集群任务方面表现出卓越的表现.
- 实验是在现实世界和合成动态网络上进行的.
- 结果验证了对动态网络表示学习提出的对比方法的有效性.
结论:
- 拟议的DNCL模型为学习动态网络表示提供了一个强大而有效的方法.
- 通过最大限度地增加相互信息,对比式学习为捕捉时间动态提供了一个强大的机制.
- 对于需要对不断变化的网络数据进行准确分析的应用程序,DNCL显示出巨大的潜力.
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