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GIPA: General Information Propagation Algorithm for Graph Learning
[article]
2021
arXiv
pre-print
Graph neural networks (GNNs) have been popularly used in analyzing graph-structured data, showing promising results in various applications such as node classification, link prediction and network recommendation. In this paper, we present a new graph attention neural network, namely GIPA, for attributed graph data learning. GIPA consists of three key components: attention, feature propagation and aggregation. Specifically, the attention component introduces a new multi-layer perceptron based
arXiv:2105.06035v2
fatcat:uscmagd2uzdzdaqjh43bffkh5a