Oct 5, 2022 · In this paper, we try to address this issue by designing an edge feature scheme and an add-on layer between every two stacked graph convolution ...
In this paper, we introduce a graph classification method by deeply exploiting the node and edge features of graphs. An edge feature scheme and an add-on layer ...
In this paper, we try to address this issue by designing an edge feature scheme and an add-on layer between every two stacked graph convolution layers in ...
Categories · Information systems not elsewhere classified · Artificial intelligence not elsewhere classified · Electrical engineering not elsewhere classified ...
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We apply our new models to graph node classification on several citation net- works, whole graph classification, and regression on sev- eral molecular datasets.
Abstract. Neural networks are typically designed to deal with data in tensor forms. In this paper, we propose a novel neural network.
In this paper, we study the problem of discriminative subgraph feature selection from uncertain graphs. This problem is challenging and different from ...
Graph neural networks, a popular class of models effective in a wide range of graph-based learning tasks, have been shown to be vulnerable to adversarial ...
Aug 10, 2024 · In this article, we address this challenge by proposing a method that constructs feature vectors based on fundamental graph structural properties.
Mixup is an advanced data augmentation method for training neural network based image classifiers, which interpolates both features and labels of a pair of ...