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在图表上解开纠的积极学习.

Haoran Yang1, Junli Wang1, Rui Duan2

  • 1Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China; National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai 201804, China.

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PubMed
概括
此摘要是机器生成的。

在图表 (DALG) 上脱而出的积极学习通过独特地解决潜在因素以更好地采样节点来增强图表学习. 这种新的方法在有限的数据下提高了模型性能,超过了现有的方法.

关键词:
积极学习是指积极学习.没有纠的功能嵌入.图形神经网络是一个神经网络.潜伏因素是一个隐藏因素.记忆列表中的记忆列表.

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科学领域:

  • 图表 机器学习 机器学习
  • 人工智能的人工智能
  • 数据科学数据科学数据科学

背景情况:

  • 图表上的积极学习 (ALG) 通过采样各种节点来解决标签稀缺问题.
  • 当前的ALG方法往往忽略了图形数据中的复杂潜伏因素,从而限制了采样效率.
  • 这可能导致节点选择不足于最佳,并错过了获取有价值数据的机会.

研究的目的:

  • 在图形学习中引入DALG (Disentangled Active Learning on Graphs),这是一种用于更有效地采样节点的新方法.
  • 解决现有的ALG方法在处理纠的潜在因素方面的局限性.
  • 提高在基于图形的机器学习任务中标记预算的效率.

主要方法:

  • 设计了Disenconv-AL层,用于学习解的功能嵌入.
  • 为每个节点构建影响图,并使用影响权重的"内存列表".
  • 根据前几轮最重要的潜伏因素进行优先抽样,以确保更广泛的覆盖范围.

主要成果:

  • 与最先进的图形主动学习方法相比,DALG实现了更高的性能.
  • 在Micro-F1和Macro-F1分数中,有大约15%的改善.
  • 在八个公共数据集上进行了广泛的实验,验证了拟议方法的有效性.

结论:

  • DALG提供了一个新的范式,用于在图表上进行主动学习,通过通过隐藏因素解来实现更细粒度的多样性.
  • 该方法在图形学习场景中增强了有限的标签预算的实用性.
  • DALG代表了强大而高效的基于图形的机器学习的重大进步.