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Node Similarity Preserving Graph Convolutional Networks
Graph Neural Networks (GNNs) have achieved tremendous success in various...
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Node2Seq: Towards Trainable Convolutions in Graph Neural Networks
Investigating graph feature learning becomes essentially important with ...
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Geom-GCN: Geometric Graph Convolutional Networks
Message-passing neural networks (MPNNs) have been successfully applied t...
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Permutohedral-GCN: Graph Convolutional Networks with Global Attention
Graph convolutional networks (GCNs) update a node's feature vector by ag...
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Rethinking Kernel Methods for Node Representation Learning on Graphs
Graph kernels are kernel methods measuring graph similarity and serve as...
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Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks
We propose a dynamic neighborhood aggregation (DNA) procedure guided by ...
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Dimensional Reweighting Graph Convolutional Networks
Graph Convolution Networks (GCNs) are becoming more and more popular for...
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Graph Neural Networks with Composite Kernels
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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