Related papers: Graph Neural Networks with Composite Kernels

Graph Neural Networks with Composite Kernels

URL: http://arxiv.org/abs/2005.07869v1
Date: Sat, 16 May 2020 04:44:29 GMT
Title: Graph Neural Networks with Composite Kernels
Authors: Yufan Zhou, Jiayi Xian, Changyou Chen, Jinhui Xu
Abstract summary: We re-interpret node aggregation from the perspective of kernel weighting. We present a framework to consider feature similarity in an aggregation scheme. We propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space.
Score: 60.81504431653264
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Learning on graph structured data has drawn increasing interest in recent years. Frameworks like Graph Convolutional Networks (GCNs) have demonstrated their ability to capture structural information and obtain good performance in various tasks. In these frameworks, node aggregation schemes are typically used to capture structural information: a node's feature vector is recursively computed by aggregating features of its neighboring nodes. However, most of aggregation schemes treat all connections in a graph equally, ignoring node feature similarities. In this paper, we re-interpret node aggregation from the perspective of kernel weighting, and present a framework to consider feature similarity in an aggregation scheme. Specifically, we show that normalized adjacency matrix is equivalent to a neighbor-based kernel matrix in a Krein Space. We then propose feature aggregation as the composition of the original neighbor-based kernel and a learnable kernel to encode feature similarities in a feature space. We further show how the proposed method can be extended to Graph Attention Network (GAT). Experimental results demonstrate better performance of our proposed framework in several real-world applications.

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