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DeepHGNN: A Novel Deep hypergraph neural network

Chinese Journal of Electronics 2022年第5期31卷 958-968页

作者： LIN Jingjing YE Zhonglin ZHAO Haixing FANG Lusheng Tibetan Information Processing and Machine Translation Key Laboratory of Qinghai Province College of Computer Qinghai Normal University Xining Urban Vocational & Technical College The State Key Laboratory of Tibetan Intelligent Information Processing and Application

With the development of deep learning,graph neural networks(GNNs) have yielded substantial results in various application fields. GNNs mainly consider the pair-wise connections and deal with graph-structured 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 intricate 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 oversmoothing, over-fitting and gradient vanishment. To tackle these issues, we present a novel deep hypergraph neural network(Deep HGNN). We design Deep HGNN 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 Deep HGNN on two visual object datasets. The experiments show the positive effects of Deep HGNN, and it works better in visual object classification tasks.

关键词： Deep neural networks Graph neural network hypergraph neural network Deep hypergraph neural network

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Make U-Net Greater: An Easy-to-Embed Approach to Improve Segmentation Performance Using hypergraph

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Computer Systems Science & Engineering 2022年第7期42卷 319-333页

作者： Jing Peng Jingfu Yang Chaoyang Xia Xiaojie Li Yanfen Guo Ying Fu Xinlai Chen Zhe Cui Chengdu Institute of Computer Application University of Chinese Academy of SciencesChengdu610041China School of Computer Science Chengdu University of Information TechnologyChengdu610225China University of Chinese Academy of Sciences Beijing100049China Department of Experimental Rheumatology Nijmegen6525GANetherlands

semantics information while maintaining spatial detail con-texts.Long-range context information plays a crucial role in this scenario.How-ever,the traditional convolution kernel only provides the local and small size of the receptiveﬁeld.To address the problem,we propose a plug-and-play module aggregating both local and global information(aka LGIA module)to capture the high-order relationship between nodes that are far apart.We incorporate both local and global correlations into hypergraph which is able to capture high-order rela-tionships between nodes via the concept of a hyperedge connecting a subset of nodes.The local correlation considers neighborhood nodes that are spatially adja-cent and similar in the same CNN feature maps of magnetic resonance(MR)image;and the global correlation is searched from a batch of CNN feature maps of MR images in feature space.The inﬂuence of these two correlations on seman-tic segmentation is complementary.We validated our LGIA module on various CNN segmentation models with the cardiac MR images dataset.Experimental results demonstrate that our approach outperformed several baseline models.

关键词： Convolutional neural network semantic segmentation hypergraph neural network LGIA module

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