Persistence Enhanced Graph Neural Network
Persistence Enhanced Graph Neural Network
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发表时间：
2020-06
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通讯作者：
Qi Zhao;Ze Ye;Chao Chen;Yusu Wang
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作者：
Qi Zhao;Ze Ye;Chao Chen;Yusu Wang
局部结构信息可以提高图的卷积网络对具有异质拓扑的大型图的适应性。现有的方法只使用相对简单的拓扑信息，如节点度。我们提出了一种新的方法，该方法利用高级拓扑信息，即持久同调，来度量图中不同部分的信息的有效性(flowffiff)。为了充分利用现实世界图中的这种结构信息，我们提出了一种新的网络结构，该结构学习使用持久的同调信息来重新加权在卷积过程中在图节点之间传递的消息。对于节点Classifi阳离子任务，我们的网络在广泛的图形基准测试中表现优于现有网络。
Local structural information can increase the adaptability of graph convolutional networks to large graphs with heterogeneous topology. Existing methods only use relatively simple topological information, such as node degrees. We present a novel approach leveraging advanced topological information, i.e., persistent homology, which measures the information flow efficiency at different parts of the graph. To fully exploit such structural information in real world graphs, we propose a new network architecture which learns to use persistent homology information to reweight messages passed between graph nodes during convolution. For node classification tasks, our network outperforms existing ones on a broad spectrum of graph benchmarks.