首页|DeepHGNN:A Novel Deep Hypergraph Neural Network
DeepHGNN:A Novel Deep Hypergraph Neural Network
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With the development of deep learning,graph neural networks(GNNs)have yielded substantial results in various application fields.GNNs mainly con-sider the pair-wise connections and deal with graph-struc-tured data.In many real-world networks,the relations between objects are complex and go beyond pair-wise.Hypergraph is a flexible modeling tool to describe intric-ate and higher-order correlations.The researchers have been concerned how to develop hypergraph-based neural network model.The existing hypergraph neural networks show better performance in node classification tasks and so on,while they are shallow network because of over-smoothing,over-fitting and gradient vanishment.To tackle these issues,we present a novel deep hypergraph neural network(DeepHGNN).We design DeepHGNN by using the technologies of sampling hyperedge,residual connection and identity mapping,residual connection and identity mapping bring from graph convolutional neural networks.We evaluate DeepHGNN on two visual object datasets.The experiments show the positive effects of DeepHGNN,and it works better in visual object classific-ation tasks.
Deep neural networksGraph neural networkHypergraph neural networkDeep hypergraph neural network
LIN Jingjing、YE Zhonglin、ZHAO Haixing、FANG Lusheng
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College of Computer,Qinghai Normal University,Xining 810008,China
The State Key Laboratory of Tibetan Intelligent Information Processing and Application,Xining 810008,China
Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province,Xining 810008,China
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