Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks
Fairness-Aware Continuous Predictions of Multiple Analytics Targets in Dynamic Networks
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DOI：
10.1145/3580305.3599341
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发表时间：
2023-08
期刊：
影响因子：
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通讯作者：
Ruifeng Liu;Qu Liu;Tingjian Ge
中科院分区：
文献类型：
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作者：
Ruifeng Liu;Qu Liu;Tingjian Ge
我们研究了一个新的问题，在动态网络中连续预测一些用户订阅的连续分析目标（CAT）。我们的架构包括任何动态图神经网络模型作为应用于网络数据的后端，以及每个CAT的前端模型，这些模型将结果与他们的信心一起返回给用户。我们设计了一种数据过滤算法，该算法将嵌入空间中可证明的最优数据子集从后端模型馈送到前端模型。其次，为了确保公平性方面的查询结果的准确性为不同的CAT和用户，我们提出了一个公平性度量和公平意识的训练调度算法，沿着的准确性保证公平估计。我们在五个真实数据集上的实验表明，我们提出的解决方案是有效的，高效的，公平的，可扩展的，和自适应的。
We study a novel problem of continuously predicting a number of user-subscribed continuous analytics targets (CATs) in dynamic networks. Our architecture includes any dynamic graph neural network model as the back end applied over the network data, and per CAT front end models that return results with their confidence to users. We devise a data filtering algorithm that feeds a provably optimal subset of data in the embedding space from back end model to front end models. Secondly, to ensure fairness in terms of query result accuracy for different CATs and users, we propose a fairness metric and a fairness-aware training scheduling algorithm, along with accuracy guarantees on fairness estimation. Our experiments over five real-world datasets show that our proposed solution is effective, efficient, fair, extensible, and adaptive.