The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.
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We demonstrate that the higher-order network embedding (HONEM) method is able to extract higher-order dependencies from HON to construct the higher-order neighborhood matrix of the network, while existing methods are not able to capture these higher-order dependencies.
We study the problem of end-to-end learning from complex multigraphs with potentially very large numbers of edges between two vertices, each edge labeled with rich information.
Network embedding is a promising way of network representation, facilitating many signed social network processing and analysis tasks such as link prediction and node classification.
More importantly, DropEdge enables us to recast a wider range of Convolutional Neural Networks (CNNs) from the image field to the graph domain; in particular, we study DenseNet and InceptionNet in this paper.
This paper proposes a method to guide tensor factorization, using class labels.
Representation learning methods that transform encoded data (e. g., diagnosis and drug codes) into continuous vector spaces (i. e., vector embeddings) are critical for the application of deep learning in healthcare.
The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.