{"id":37717,"date":"2022-10-08T22:00:01","date_gmt":"2022-10-08T14:00:01","guid":{"rendered":"https:\/\/swarma.org\/?p=37717"},"modified":"2022-10-08T22:00:01","modified_gmt":"2022-10-08T14:00:01","slug":"%e6%9c%ba%e5%99%a8%e8%83%bd%e5%a4%9f%e5%ad%a6%e4%b9%a0%e5%87%ba%e7%bb%9f%e8%ae%a1%e5%8a%9b%e5%ad%a6%e5%90%97%ef%bc%9f-%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e7%9a%84%e5%a4%9a%e5%b0%ba","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=37717","title":{"rendered":"\u673a\u5668\u80fd\u591f\u5b66\u4e60\u51fa\u7edf\u8ba1\u529b\u5b66\u5417\uff1f\u2014\u2014\u590d\u6742\u7cfb\u7edf\u7684\u591a\u5c3a\u5ea6\u5efa\u6a21\u4e0e\u590d\u6742\u7f51\u7edc\u7684\u591a\u5c3a\u5ea6\u94fe\u8def\u9884\u6d4b \uff5c\u5468\u65e5\u00b7\u56e0\u679c\u6d8c\u73b0\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u3010\u5927\u7eb2\u3011<\/strong><\/span><\/section>\n
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GNN\u57fa\u7840\uff1a\u56fe\u4e0a\u7684\u5377\u79ef\u64cd\u4f5c<\/span><\/section>\n

\u57fa\u4e8e\u8c31\uff08Spectral\uff09\u7684\u5377\u79ef<\/span><\/p>\n

\u57fa\u4e8e\u6d88\u606f\u4f20\u9012\uff08Message Passing\uff09\u7684\u7a7a\u95f4\u57df\uff08Spatial\uff09\u5377\u79ef<\/span><\/p>\n

\u56fe\u6c60\u5316\uff08Graph Pooling\uff09<\/span><\/section>\n

\u56fe\u6c60\u5316\u6982\u8ff0<\/span><\/p>\n

Flat pooling (Graph readout)<\/span><\/p>\n

Node drop pooling: Graph U-net\u4e2d\u7684Top-K\u6c60\u5316    Node clustering pooling: DiffPool<\/span><\/p>\n

\u56fe\u7c97\u7c92\u5316\u5728\u94fe\u8def\u9884\u6d4b\u4e2d\u7684\u5e94\u7528<\/span><\/section>\n

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Weisfeiler-Lehman Neural <\/span><\/p>\n

Machine \uff08WLNM\uff09<\/span><\/section>\n

SEAL\u6846\u67b6<\/span><\/p>\n

A Multi-Scale Approach for Graph Link Prediction<\/span><\/p>\n

\u603b\u7ed3<\/span><\/section>\n

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\u3010\u4e3b\u8981\u6d89\u53ca\u5230\u7684\u77e5\u8bc6\u6982\u5ff5\u3011<\/span><\/strong><\/span><\/section>\n
\u5377\u79ef\u795e\u7ecf\u7f51\u7edc Convolutional Neural Networks<\/span><\/section>\n
\u6c60\u5316 Pooling<\/span><\/section>\n
\u56fe\u795e\u7ecf\u7f51\u7edc Graph Neural Networks<\/span><\/section>\n
\u57fa\u4e8e\u8c31\u7684\u56fe\u5377\u79ef Spectral-based Graph Convolution<\/span><\/section>\n
\u56fe\u6c60\u5316 Graph Pooling<\/span><\/section>\n
\u94fe\u8def\u9884\u6d4b Link Prediction<\/span><\/section>\n


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\u4e3b\u8bb2\u4eba\u4ecb\u7ecd<\/span><\/strong><\/span><\/h3>\n
<\/section>\n
\u4fee\u5b87\u7487<\/span><\/strong>\uff0c\u6e05\u534e-\u4f2f\u514b\u5229\u6df1\u5733\u5b66\u9662\u535a\u58eb\u7814\u7a76\u751f\u5728\u8bfb\uff0c\u4e3b\u8981\u7684\u7814\u7a76\u65b9\u5411\u662f\u590d\u6742\u7f51\u7edc\u7684\u94fe\u8def\u9884\u6d4b\u53ca\u5176\u5728\u6280\u672f\u521b\u65b0\u7ba1\u7406\u9886\u57df\u7684\u5e94\u7528\u3002<\/span><\/section>\n


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\u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/h3>\n
[1]X. Chen, \u201cUnderstanding Spectral Graph Neural Network,\u201d Sep. 2021. doi: 10.13140\/RG.2.2.27579.03364\/1.<\/span><\/section>\n
[2]H. Gao and S. Ji, \u201cGraph U-Nets,\u201d in Proceedings of the 36th International Conference on Machine Learning, May 2019, pp. 2083\u20132092.<\/span><\/section>\n
[3]Cai, Lei, and Shuiwang Ji. “A multi-scale approach for graph link prediction.” In Proceedings of the AAAI conference on artificial intelligence, vol. 34, no. 04, pp. 3308-3315. 2020.<\/span><\/section>\n


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\u3010\u5927\u7eb2\u3011<\/span><\/strong><\/span><\/section>\n
\u590d\u6742\u7cfb\u7edf\u4ee5\u53ca\u5927\u89c4\u6a21\u4eff\u771f \/Complex systems<\/span><\/section>\n
\u590d\u6742\u7cfb\u7edf\u7684\u591a\u5c3a\u5ea6\u4eff\u771f\/Multiscale simulation for complex systems<\/span><\/section>\n

\u76f8\u5173\u80cc\u666f\u77e5\u8bc6\uff08What\/Why\/How\uff09\/  Multiscale simulation<\/span><\/p>\n

\u591a\u5c3a\u5ea6\u5efa\u6a21\u65b9\u6cd5\u4ecb\u7ecd \/ Muliscale simulation methods<\/span><\/p>\n

\u7c97\u5316\u65b9\u6cd5\u5bf9\u6bd4 \/ Coarsen method comparison<\/span><\/p>\n

\u66f4\u6709\u6548\u7684\u591a\u5c3a\u5ea6\u5efa\u6a21\u65b9\u6cd5\/ Effective multiscale simulation<\/span><\/section>\n

\u6311\u6218 \/Challenge<\/span><\/p>\n

\u89e3\u51b3\u65b9\u6848\u4e0e\u6846\u67b6\/Proposed solution and framework<\/span><\/p>\n

\u5b9e\u9a8c\u7ed3\u679c\u5206\u6790\/ Result analysis<\/span><\/p>\n

\u4e00\u5f20\u56fe\u603b\u7ed3 \/ The whole picture of coarsen method comparison<\/span><\/section>\n
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\u3010\u6d89\u53ca\u5230\u7684\u77e5\u8bc6\u6982\u5ff5\u3011<\/span><\/strong><\/span><\/section>\n
\u590d\u6742\u7cfb\u7edf  Complex system<\/span><\/section>\n
\u591a\u5c3a\u5ea6\u4eff\u771f  Multiscale simulation<\/span><\/section>\n
\u7c97\u5316\u65b9\u6cd5  Coarsen methods<\/span><\/section>\n
\u673a\u5668\u5b66\u4e60  Machine learning<\/span><\/section>\n


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\u53c2\u8003\u6587\u732e<\/span><\/strong><\/h3>\n
[1] Vlachas, Pantelis R. , et al. “Multiscale Simulations of Complex Systems by Learning their Effective Dynamics.” (2020).<\/span><\/section>\n
[2] Engquist, Bjorn , et al. “Heterogeneous Multiscale Methods: A Review.” Communications in Computational Physics, 2007, 2(3): 367-450.<\/span><\/section>\n
[3]Aditya, Bindala, Marianthi G.Ierapetritou, et al.”Equation-free, coarse-grained computational optimization using timesteppers”,Chemical Engineering Science,Vol.61, no.2, January 2006, pp. 779-793<\/span><\/section>\n

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