在图形嵌入和链接预测中比较随机步行
Adilson Vital1, Filipi Nascimento Silva2, Diego Raphael Amancio1
1Institute of Mathematics and Computer Science, USP, São Carlos, SP, Brazil.
PloS one
|November 6, 2024
概括
这项研究比较了网络分析的随机步行策略. 研究结果显示,各种步行偏差对链接预测准确度的影响很小,突出了网络结构.
科学领域:
- 网络科学 网络科学
- 数据挖掘 数据挖掘
- 机器学习 机器学习
背景情况:
- 随机步行对于复杂的网络分析至关重要,特别是在嵌入生成和链接预测方面.
- 各种随机步行偏差对嵌入质量和下游链接预测性能的具体影响尚未得到充分理解.
研究的目的:
- 为了比较分析不同随机步行策略对嵌入生成和链接预测的影响.
- 调查诸如节点程度和自我回避等偏差如何影响网络表示.
主要方法:
- 评估了传统的随机走路,自我避开的随机走路和程度偏差的随机走路.
- 调整了 node2vec 算法,以探索各种随机步行行为.
- 在链接预测任务中评估生成嵌入的性能.
主要成果:
- 尽管随机步行行为和嵌入特征存在显著差异,但链接预测性能在各个策略中只显示了微小的差异.
- 网络结构似乎可以稳定地恢复,而不管使用的特定随机步行启发式.
结论:
- 随机走路策略的选择对复杂网络中链接预测的有效性产生了有限的影响.
- 从未知底层机制的序列生成的网络数据可以使用这些方法可靠地重建.
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