{"id":53046,"date":"2024-10-08T20:50:42","date_gmt":"2024-10-08T12:50:42","guid":{"rendered":"https:\/\/swarma.org\/?p=53046"},"modified":"2024-10-08T20:50:42","modified_gmt":"2024-10-08T12:50:42","slug":"%e6%95%b0%e6%8d%ae%e9%a9%b1%e5%8a%a8%e7%9a%84%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e9%a2%84%e6%b5%8b%ef%bc%9a%e9%95%bf%e6%97%b6%e6%bc%94%e5%8c%96%e4%b8%8e%e9%9f%a7%e6%80%a7%ef%bd%9c%e5%91%a8%e5%9b%9b","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=53046","title":{"rendered":"\u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u9884\u6d4b\uff1a\u957f\u65f6\u6f14\u5316\u4e0e\u97e7\u6027\uff5c\u5468\u56db\u76f4\u64ad\u00b7\u590d\u6742\u7cfb\u7edf\u81ea\u52a8\u5efa\u6a21\u8bfb\u4e66\u4f1a\u7b2c\u4e8c\u5b63"},"content":{"rendered":"
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Part 1 \u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u590d\u6742\u7cfb\u7edf\u957f\u65f6\u9884\u6d4b<\/strong><\/span><\/section>\n
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Part 2 \u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u97e7\u6027\u9884\u6d4b<\/strong><\/span><\/section>\n
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Part 1 \u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u590d\u6742\u7cfb\u7edf\u957f\u65f6\u9884\u6d4b<\/strong><\/span><\/section>\n
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Part 2 \u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u97e7\u6027\u9884\u6d4b<\/strong><\/span><\/section>\n
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Part 1 \u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u590d\u6742\u7cfb\u7edf\u957f\u65f6\u9884\u6d4b<\/strong><\/span><\/section>\n
\u4e0d\u53d8\u91cf\/\u6162\u53d8\u91cf\uff08Invariant\/Slow Variable\uff09<\/span><\/section>\n
\u795e\u7ecf\u5e38\u5fae\u5206\u65b9\u7a0b\uff08Neural ODE\uff09<\/span><\/section>\n
\u91cd\u6574\u5316\u7fa4\uff08Renormalization Group\uff09<\/span><\/section>\n

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Part 2 \u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u97e7\u6027\u9884\u6d4b<\/strong><\/span><\/section>\n
\u7f51\u7edc\u97e7\u6027\uff08Network Resilience\uff09<\/span><\/section>\n
\u56fe\u795e\u7ecf\u7f51\u7edc \uff08Graph Neural Networks\uff09<\/span><\/section>\n
\u751f\u6210\u6269\u6563\u6a21\u578b\uff08Diffusion Models\uff09<\/span><\/section>\n


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\u674e\u745e\u5803<\/span><\/strong>\uff0c\u6e05\u534e\u5927\u5b66\u6df1\u5733\u56fd\u9645\u7814\u7a76\u751f\u966223\u7ea7\u7855\u58eb\u751f\uff0c\u5e08\u4ece\u5ed6\u5e86\u654f\u6559\u6388\uff0c\u73b0\u4e8e\u6e05\u534e\u5927\u5b66\u7535\u5b50\u7cfb\u6570\u636e\u79d1\u5b66\u4e0e\u667a\u80fd\u5b9e\u9a8c\u5ba4\u8bbf\u95ee\u5b9e\u4e60\u30022023\u5e74\u5728\u534e\u4e2d\u79d1\u6280\u5927\u5b66\u83b7\u5f97\u5de5\u5b66\u5b66\u58eb\u5b66\u4f4d\u3002\u7814\u7a76\u9886\u57df\u96c6\u4e2d\u5728AI for Complex System\uff0c\u7814\u7a76\u5174\u8da3\u4e3a\u6570\u636e\u6316\u6398\u548c\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\uff0c\u5305\u62ec\u6570\u636e\u9a71\u52a8\u7684\u7cfb\u7edf\u5efa\u6a21\u548c\u5927\u89c4\u6a21\u7f51\u7edc\u964d\u7ef4\u3002\u4ee5\u7b2c\u4e00\u4f5c\u8005\u5728\u56fd\u9645\u77e5\u8bc6\u53d1\u73b0\u4e0e\u6570\u636e\u6316\u6398\u5927\u4f1aSIGKDD 2023\u30012024\u4e0a\u53d1\u8868\u591a\u7bc7\u6587\u7ae0\u3002<\/span><\/section>\n

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\u5218\u7545<\/span><\/strong>\uff0c\u6e05\u534e\u5927\u5b66\u7535\u5b50\u5de5\u7a0b\u7cfb23\u7ea7\u535a\u58eb\u7814\u7a76\u751f\uff0c\u5e08\u4ece\u674e\u52c7\u6559\u6388\u30022023\u5e74\u5728\u6e05\u534e\u5927\u5b66\u83b7\u5f97\u5de5\u5b66\u5b66\u58eb\u5b66\u4f4d\u3002\u7814\u7a76\u9886\u57df\u96c6\u4e2d\u5728AI for Complex System\uff0c\u7814\u7a76\u5174\u8da3\u4e3a\u6570\u636e\u6316\u6398\u548c\u590d\u6742\u7cfb\u7edf\u5efa\u6a21\uff0c\u5305\u62ec\u6570\u636e\u9a71\u52a8\u7684\u7cfb\u7edf\u5efa\u6a21\uff0c\u590d\u6742\u7cfb\u7edf\u97e7\u6027\uff0c\u7269\u7406\u542f\u53d1\u7684\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u7b49\u3002\u4ee5\u7b2c\u4e00\u4f5c\u8005\u5728SIGKDD2022\u30012024\u4e0a\u53d1\u8868\u591a\u7bc7\u6587\u7ae0\u3002<\/span><\/section>\n


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Part 1 \u57fa\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u590d\u6742\u7cfb\u7edf\u957f\u65f6\u9884\u6d4b<\/strong><\/span><\/p>\n

Li, Ruikun<\/strong>, Huandong Wang, and Yong Li. “Learning slow and fast system dynamics via automatic separation of time scales.” Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining<\/em>. 2023.<\/span><\/p>\n

Kostic, Vladimir R., et al. “Learning invariant representations of time-homogeneous stochastic dynamical systems.” ICLR 2024<\/em>. 2024.<\/span><\/p>\n

Garc\u00eda-P\u00e9rez, Guillermo, Mari\u00e1n Bogu\u00f1\u00e1, and M. \u00c1ngeles Serrano. “Multiscale unfolding of real networks by geometric renormalization.” Nature Physics<\/em> 14.6 (2018): 583-589.<\/span><\/p>\n

Li, Ruikun<\/strong>, et al. “Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space.” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining<\/em>. 2024.<\/span><\/section>\n

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Part 2 \u6570\u636e\u9a71\u52a8\u7684\u590d\u6742\u7cfb\u7edf\u97e7\u6027\u9884\u6d4b<\/strong><\/span><\/p>\n

Liu, Chang<\/strong>, et al. “TDNetGen: Empowering Complex Network Resilience Prediction with Generative Augmentation of Topology and Dynamics.” Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining<\/em>. 2024.<\/span><\/p>\n

Gao, Jianxi, Baruch Barzel, and Albert-L\u00e1szl\u00f3 Barab\u00e1si. “Universal resilience patterns in complex networks.” Nature<\/em> 530.7590 (2016): 307-312.<\/span><\/p>\n

Liu, Gang, et al. “Data-centric learning from unlabeled graphs with diffusion model.” Advances in neural information processing systems<\/em> 36 (2024).<\/span><\/section>\n


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\u6591\u56fe\u94fe\u63a5\uff1ahttps:\/\/pattern.swarma.org\/study_group_issue\/769?from=wechat<\/span><\/section>\n

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