Towards explainable community finding.
Towards explainable community finding.
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DOI：
10.1007/s41109-022-00515-6
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发表时间：
2022
影响因子：
2.2
通讯作者：
中科院分区：
文献类型：
--
作者：
节点社区的检测是理解网络结构的一项重要任务。已经开发了多种方法来解决这个问题，其中许多方法在现实世界的应用中常用，例如在公共卫生网络中。然而，这些算法产生的社区标签背后的推理很少提供清晰的见解。从机器学习文献中汲取灵感，我们的目标是使用网络的可解释特征为这些算法的输出提供事后解释。在本文中，我们提出了一个模型不可知的方法，确定了一组信息功能，以帮助解释社区发现算法的输出。我们将其应用到三个著名的算法，虽然该方法的目的是推广到新的方法。以及确定重要功能的事后解释系统，我们报告的共同特点，发现不同的算法和方法之间的差异。在线版本包含补充材料，可通过10.1007/s41109-022-00515-6获得。
The detection of communities of nodes is an important task in understanding the structure of networks. Multiple approaches have been developed to tackle this problem, many of which are in common usage in real-world applications, such as in public health networks. However, clear insight into the reasoning behind the community labels produced by these algorithms is rarely provided. Drawing inspiration from the machine learning literature, we aim to provide post-hoc explanations for the outputs of these algorithms using interpretable features of the network. In this paper, we propose a model-agnostic methodology that identifies a set of informative features to help explain the output of a community finding algorithm. We apply it to three well-known algorithms, though the methodology is designed to generalise to new approaches. As well as identifying important features for a post-hoc explanation system, we report on the common features found made by the different algorithms and the differences between the approaches. The online version contains supplementary material available at 10.1007/s41109-022-00515-6.
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影响因子：
1
作者：
Brandes, U
通讯作者：
Brandes, U
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影响因子：
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影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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通讯作者：
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