论文标题
预后：使用图指标预测GNN计算时间的数据驱动模型
ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics
论文作者
论文摘要
图形神经网络（GNN）在处理图形结构数据的问题方面表现出巨大的希望。 GNN的独特点之一是它们适应多个问题的灵活性，这不仅会导致广泛的适用性，而且在为特定问题找到最佳模型或加速技术时会带来重要的挑战。这种挑战的一个例子在于，GNN模型或加速技术的准确性或有效性通常取决于基础图的结构。在本文中，为了解决图形依赖性加速度的问题，我们提出了预后，这是一个数据驱动的模型，可以通过检查输入图指标来预测给定GNN模型的GNN训练时间。此类预测是基于先前使用多样化的合成图数据集对离线训练的回归做出的。在实践中，我们的方法允许对特定问题进行明智的决定做出明智的决定。在本文中，为特定用例定义并应用了构建预后的方法，其中有助于确定哪种图表更好。我们的结果表明，预后有助于在多种广泛使用的GNN模型（例如GCN，GIN，GAT或GRAPHSAGE）中随机选择图表的平均加速度为1.22倍。
Graph Neural Networks (GNN) show great promise in problems dealing with graph-structured data. One of the unique points of GNNs is their flexibility to adapt to multiple problems, which not only leads to wide applicability, but also poses important challenges when finding the best model or acceleration technique for a particular problem. An example of such challenges resides in the fact that the accuracy or effectiveness of a GNN model or acceleration technique generally depends on the structure of the underlying graph. In this paper, in an attempt to address the problem of graph-dependent acceleration, we propose ProGNNosis, a data-driven model that can predict the GNN training time of a given GNN model running over a graph of arbitrary characteristics by inspecting the input graph metrics. Such prediction is made based on a regression that was previously trained offline using a diverse synthetic graph dataset. In practice, our method allows making informed decisions on which design to use for a specific problem. In the paper, the methodology to build ProGNNosis is defined and applied for a specific use case, where it helps to decide which graph representation is better. Our results show that ProGNNosis helps achieve an average speedup of 1.22X over randomly selecting a graph representation in multiple widely used GNN models such as GCN, GIN, GAT, or GraphSAGE.
