Aug 27, 2019 · Graph Convolutional Network (GCN) has been proposed as a powerful method for graph-based semi-supervised learning, which has the similar operation and ...
Combining supervised term and unsupervised term, our proposed model includes more intrinsic properties of graph-structured data and improves the GCN model with ...
Feb 5, 2024 · Bibliographic details on PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning.
Mar 27, 2023 · On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs. On ...
Recently, Graph Convolutional Network (GCN) has been proposed as a powerful method for graph-based semi-supervised learning, which has the similar operation and ...
Nov 3, 2022 · PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning. Article 27 August 2019. Use our pre ...
Jul 18, 2024 · PKGCN [6] decomposes the objective function of semi-supervised learning into supervised and unsupervised terms by using priori knowledge, so ...
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A graph residual generation network for node classification based ... - OUCI
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PKGCN: prior knowledge enhanced graph convolutional network for graph-based semi-supervised learning. International Journal of Machine Learning and ...
Graph Convolutional Neural Networks (graph CNNs) have been widely used for graph data representation and semi-supervised learning tasks. However, existing ...
Missing: PKGCN: | Show results with:PKGCN:
Sep 9, 2016 · We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks.
Missing: PKGCN: prior enhanced