论文标题
通过迭代均匀的图形神经网络朝着与图形相关的问题相关的问题解决
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous Graph Neural Networks
论文作者
论文摘要
当解决许多图形分析问题时，目前的图形神经网络（GNN）缺乏有关量表（图形大小，图形直径，边缘重量等）的普遍性。从综合图理论程序的角度来看，我们提出了几个扩展来解决该问题。首先，灵感来自迭代数量的依赖性，共同图理论算法对图形大小的依赖性，我们学会根据计算进度适应GNN中的消息传递过程。其次，灵感来自以下事实：许多图理论算法在图形权重方面都是均一的，我们引入了均匀的转换层，这些转换层是通用均匀函数近似值，以将普通的GNN转换为均匀。在实验上，我们表明我们的GNN可以从小型图中训练，但可以很好地推广到大规模图，以了解许多基本的图理论问题。它还显示了多体物理模拟和基于图像的导航问题的应用的普遍性。
Current graph neural networks (GNNs) lack generalizability with respect to scales (graph sizes, graph diameters, edge weights, etc..) when solving many graph analysis problems. Taking the perspective of synthesizing graph theory programs, we propose several extensions to address the issue. First, inspired by the dependency of the iteration number of common graph theory algorithms on graph size, we learn to terminate the message passing process in GNNs adaptively according to the computation progress. Second, inspired by the fact that many graph theory algorithms are homogeneous with respect to graph weights, we introduce homogeneous transformation layers that are universal homogeneous function approximators, to convert ordinary GNNs to be homogeneous. Experimentally, we show that our GNN can be trained from small-scale graphs but generalize well to large-scale graphs for a number of basic graph theory problems. It also shows generalizability for applications of multi-body physical simulation and image-based navigation problems.
