Hierarchical Graph Convolutional Network for Data Evaluation of Dynamic Graphs
Hierarchical Graph Convolutional Network for Data Evaluation of Dynamic Graphs
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DOI：
10.1109/bigdata50022.2020.9377789
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Bin Wang-;Teruaki Hayashi;Y. Ohsawa
中科院分区：
文献类型：
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作者：
Bin Wang-;Teruaki Hayashi;Y. Ohsawa
随着数据以惊人的速度和规模产生，旨在充分发现数据价值的数据市场变得越来越重要。数据评估是数据市场的重要组成部分。它可以发现数据的缺陷和隐藏在数据中的价值。然而，很少有研究调查如何评估数据市场中的数据。现有的方法很少利用数据的多层结构。我们的工作提出了一种新的层次图卷积网络的动态图的数据评估，以下的异常检测范式。我们的模型在几个基准数据集上的表现明显优于现有模型。该研究为动态图形的处理提供了一个更有力的工具。它还可以指导数据市场中更广泛数据类别的数据评估方向。
As data are being generated at an incredible speed and scale, the market of data that aims to fully discover the value of data is becoming increasingly important. Data evaluation is a vital part of the data market. It can discover the data’s flaws and the value hidden in the data. Nevertheless, there have been few studies investigating how to evaluate data in the data market. Existing methods seldom utilize the multi-level structure in data. Our work proposes a novel hierarchical graph convolutional network for the data evaluation of dynamic graphs, following the anomaly detection paradigm. Our model performs significantly better than existing models on several benchmark datasets. This study provides a more powerful tool for processing dynamic graphs. It could also guide the direction of data evaluation for a broader range of data categories in the data market.