Besides the backbone network consisting of spectral hypergraph convolution blocks, a hyperedge attention module is learned to adjust the weights of hyperedges in the WHCN. Finally, a segmentation network is trained by these pseudo point cloud labels. Experimental results on the scanNet, S3DIS, ...
This work develops a based on Heterogeneous hypergraph convolution and multi-order convolution of heterogeneous graph model, namely HHMDA to perform a MiRNA-Disease Association prediction task. Example To run HHMDAon your data, execute the following command from the project home directory: 'python ...
PyTorch Geometric:https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers(Hypergraph Convolution Network) DeepHypergraph:https://github.com/iMoonLab/DeepHypergraph(Hypergraph Neural Networks) OpenHGNN:https://github.com/BUPT-GAMMA/OpenHGNN(Heterogeneous Graph Neural Network) ...
PyTorch Geometric:https://pytorch-geometric.readthedocs.io/en/latest/modules/nn.html#convolutional-layers(Hypergraph Convolution Network) DeepHypergraph:https://github.com/iMoonLab/DeepHypergraph(Hypergraph Neural Networks) OpenHGNN:https://github.com/BUPT-GAMMA/OpenHGNN(Heterogeneous Graph Neural Network)...
to alleviate the issue of data scarcity, we incorporate an external knowledge graph and construct aknowledge-based hypergraphconsidering fine-grained, entity-level semantics. We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to...
In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. In this ...
Building the Convolution Layer of HGNN+ classHGNNPConv(nn.Module):def__init__(self,):super().__init__() ...self.reset_parameters()defforward(self,X:torch.Tensor,hg:dhg.Hypergraph)->torch.Tensor:# apply the trainable parameters ``theta`` to the input ``X``X=self.theta(X)# perform...
This work has been published in IJCAI 2019. Dynamic Hypergraph Neural Networks (DHGNN) is a kind of neural networks modeling dynamically evolving hypergraph structures, which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). ...
Then, a graph convolution is run on the heterogeneous graph to capture the multi-scale feature representations of miRNA and disease. MiRNA-disease association are reconstructed based on these features. Meanwhile, HHMDA constructs a heterogeneous hypergraph with miRNAs and diseases as nodes, and the ...
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