Computer Science > Machine Learning
[Submitted on 17 Jun 2022 (v1), last revised 29 Sep 2022 (this version, v3)]
Title:ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs
View PDFAbstract:Many real-world data can be modeled as 3D graphs, but learning representations that incorporates 3D information completely and efficiently is challenging. Existing methods either use partial 3D information, or suffer from excessive computational cost. To incorporate 3D information completely and efficiently, we propose a novel message passing scheme that operates within 1-hop neighborhood. Our method guarantees full completeness of 3D information on 3D graphs by achieving global and local completeness. Notably, we propose the important rotation angles to fulfill global completeness. Additionally, we show that our method is orders of magnitude faster than prior methods. We provide rigorous proof of completeness and analysis of time complexity for our methods. As molecules are in essence quantum systems, we build the \underline{com}plete and \underline{e}fficient graph neural network (ComENet) by combing quantum inspired basis functions and the proposed message passing scheme. Experimental results demonstrate the capability and efficiency of ComENet, especially on real-world datasets that are large in both numbers and sizes of graphs. Our code is publicly available as part of the DIG library (\url{this https URL}).
Submission history
From: Limei Wang [view email][v1] Fri, 17 Jun 2022 02:35:03 UTC (556 KB)
[v2] Sat, 17 Sep 2022 15:42:53 UTC (813 KB)
[v3] Thu, 29 Sep 2022 00:30:18 UTC (813 KB)
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