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MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning. (arXiv:2106.09289v2 [cs.LG] UPDATED)
cs.LG updates on arXiv.org arxiv.org
The attention mechanism enables graph neural networks (GNNs) to learn the
attention weights between the target node and its one-hop neighbors, thereby
improving the performance further. However, most existing GNNs are oriented
toward homogeneous graphs, and in which each layer can only aggregate the
information of one-hop neighbors. Stacking multilayer networks introduces
considerable noise and easily leads to over smoothing. We propose here a
multihop heterogeneous neighborhood information fusion graph representation
learning method (MHNF). Specifically, we propose a hybrid metapath …
arxiv fusion graph graph representation information learning lg representation representation learning