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新视角和链接预测方法
New Perspectives and Methods in Link Prediction |
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| 课程网址: |
http://videolectures.net/kdd2010_lichtenwalter_npml/
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| 主讲教师: |
Ryan Lichtenwalter
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| 开课单位: |
圣母大学
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| 开课时间: |
2010-10-01
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| 课程语种: |
英语
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| 中文简介: |
本文考察了网络中链接预测的重要因素, 为预测任务提供了一个通用、高性能的框架。稀疏网络中的链接预测带来了重大挑战, 因为可以形成的链接与确实形成的链接存在固有的不比例。此前的研究通常将此视为一个无人监督的问题。虽然这不是第一次探索监督学习的工作, 但影响和指导分类的许多重要因素仍未探索。在本文中, 我们首先通过仔细研究网络观察期、现有方法的普遍性、方差缩小、拓扑原因和度数等问题来考虑这些因素。不平衡, 并采用抽样方法。我们还提出了一种有效的基于流的预测算法, 为稀疏网络链路预测中的不平衡提供了形式边界, 并采用了适合观测到的不平衡的评价方法。我们对上述问题的仔细考虑最终导致了一个完全通用的框架, 其性能超过了30% 以上的非监督链接预测方法。
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| 课程简介: |
This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task. Link prediction in sparse networks presents a significant challenge due to the inherent disproportion of links that can form to links that do form. Previous research has typically approached this as an unsupervised problem. While this is not the first work to explore supervised learning, many factors significant in influencing and guiding classification remain unexplored. In this paper, we consider these factors by first motivating the use of a supervised framework through a careful investigation of issues such as network observational period, generality of existing methods, variance reduction, topological causes and degrees of imbalance, and sampling approaches. We also present an effective flow-based predicting algorithm, offer formal bounds on imbalance in sparse network link prediction, and employ an evaluation method appropriate for the observed imbalance. Our careful consideration of the above issues ultimately leads to a completely general framework that outperforms unsupervised link prediction methods by more than 30% AUC.
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| 关 键 词: |
计算机科学; 数据挖掘; 链接预测
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-01：吴雨秋(课程编辑志愿者)
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| 阅读次数: |
219
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