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Powerful graph of graphs neural network for structured entity analysis


Structured entities analysis is the basis of the modern science, such as chemical science, biological science, environmental science and medical science. Recently, a huge amount of computational models have been proposed to analyze structured entities such as chemical molecules and proteins. However, the problem becomes complex when local structural entity graphs and a global entity interaction graph are both involved. The unique graph of graphs structure cannot be properly exploited by most existing works for structural entity analysis. Some works that build neural networks on the graph of graphs cannot preserve the local graph structure effectively, hence, reducing the expressive power of the model. In this paper, we propose a Powerful Graph Of graphs neural Network, namely PGON, which has 3-Weisfeiler-Lehman expressive power and captures the attributes and structural information from both structured entity graphs and entity interaction graph hierarchically. Extensive experiments are conducted on real-world datasets, which show that PGON outperforms other state-of-the-art methods on both graph classification and graph interaction prediction tasks.

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Correspondence to Chen Chen.

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This article belongs to the Topical Collection: Special Issue on Large Scale Graph Data Analytics Guest Editors: Xuemin Lin, Lu Qin, Wenjie Zhang, and Ying Zhang

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Wang, H., Lian, D., Liu, W. et al. Powerful graph of graphs neural network for structured entity analysis. World Wide Web 25, 609–629 (2022).

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