{"id":16631,"date":"2019-08-07T20:04:36","date_gmt":"2019-08-08T09:51:12","guid":{"rendered":"https:\/\/swarma.org\/?p=16631"},"modified":"2019-08-08T17:51:40","modified_gmt":"2019-08-08T09:51:40","slug":"8b1052a734","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=16631","title":{"rendered":"\u56fe\u7f51\u7edc\u5982\u4f55\u52a9\u529b\u57ce\u5e02\u5927\u8111\uff1a\u8de8\u65f6\u7a7a\u5927\u5c3a\u5ea6\u7f51\u7ea6\u8f66\u6d41\u91cf\u9884\u6d4b"},"content":{"rendered":"
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\u636e Uber \u7edf\u8ba1\uff0c2018\u5e74\uff0c\u5168\u5e74\u7684\u7f51\u7ea6\u8f66\u4f7f\u7528\u6b21\u6570\u8fbe180\u4ebf\u6b21\uff0c\u8d85\u8fc7\u5168\u7403\u4eba\u53e3\u7684\u4e24\u500d\u3002\u51c6\u786e\u7684\u7f51\u7ea6\u8f66\u8ba2\u5355\u9884\u6d4b\uff0c\u80fd\u591f\u66f4\u597d\u7684\u8c03\u5ea6\u8f66\u8f86\uff0c\u63d0\u9ad8\u8f66\u8f86\u7684\u5229\u7528\u7387\uff0c\u51cf\u5c11\u7b49\u5f85\u65f6\u95f4\uff0c\u4ece\u800c\u7f13\u89e3\u4ea4\u901a\u62e5\u5835\uff0c\u5177\u6709\u91cd\u8981\u7684\u7ecf\u6d4e\u548c\u793e\u4f1a\u610f\u4e49\u3002\u8fd1\u5e74\u6765\u5927\u706b\u7684\u56fe\u795e\u7ecf\u7f51\u7edc GNN \u76f8\u5f53\u4e8e\u63d0\u4f9b\u4e86\u4e00\u79cd\u65b0\u7684\u5efa\u6a21\u548c\u89e3\u51b3\u65f6\u7a7a\u6570\u636e\u9884\u6d4b\u7684\u624b\u6bb5\u3002<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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ST-MGCN \u662f\u6ef4\u6ef4\u51fa\u884c AI Lab \u53d1\u8868\u4e8e AAAI 2019 \u7684\u4e00\u79cd\u57fa\u4e8e\u65f6\u7a7a\u591a\u56fe\u5377\u79ef\u7f51\u7edc\u7684\u7f51\u7ea6\u8f66\u9700\u6c42\uf97e\u9884\u6d4b\u6a21\u578b\u3002\u533a\u57df\u7ea7\u9700\u6c42\u9884\u6d4b\u662f\u7f51\u7ea6\u8f66\u670d\u52a1\u7684\u5173\u952e\u6280\u672f\u3002\u51c6\u786e\u7684\u7f51\u7ea6\u8f66\u9700\u6c42\u91cf\u9884\u6d4b\u53ef\u4ee5\u6307\u5bfc\u8f66\u8f86\u7684\u8c03\u5ea6\uff0c\u63d0\u9ad8\u8f66\u8f86\u7684\u5229\u7528\u7387\uff0c\u51cf\u5c11\u7b49\u5f85\u65f6\u95f4\uff0c\u4ee5\u53ca\u7f13\u89e3\u4ea4\u901a\u62e5\u5835\u3002\u533a\u57df\u4e4b\u95f4\u6240\u5b58\u5728\u590d\u6742\u7684\u65f6\u7a7a\u4f9d\u8d56\u5173\u7cfb\u4f7f\u5f97\u8fd9\u4e2a\u95ee\u9898\u5177\u6709\u5f88\u5927\u6311\u6218\u3002\u5df2\u6709\u65b9\u6cd5\u4e3b\u8981\u5173\u6ce8\u4e8e\u5efa\u6a21\u76f8\u90bb\u533a\u57df\u5728\u7a7a\u95f4\u4e0a\u7684\u6b27\u5f0f\u76f8\u5173\u6027\uff08Euclidean correlations\uff09\uff0c\u800c\u4f5c\u8005\u53d1\u73b0\u8ddd\u79bb\u8f83\u8fdc\u533a\u57df\u4e4b\u95f4\u7684\u975e\u6b27\u76f8\u5173\u6027\u4e5f\u5bf9\u9884\u6d4b\u81f3\u5173\u91cd\u8981\u3002<\/span><\/p>\n

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Spatiotemporal Multi-Graph Convolution Network for Ride-hailing Demand Forecasting<\/span><\/p>\n

\u8bba\u6587\u5730\u5740\uff1a<\/span><\/p>\n

http:\/\/www-scf.usc.edu\/~yaguang\/papers\/<\/span><\/p>\n

aaai19_multi<\/span>_<\/span>graph_<\/span>convolution.pdf<\/span><\/p>\n<\/section>\n<\/section>\n<\/blockquote>\n

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\u672c\u6587\u63d0\u51fa\u4e86\u65f6\u7a7a\u591a\u56fe\u5377\u673a\u7f51\u7edc spatiotemporal multi-graph convolution network (ST-MGCN)\uff0c\u4e00\u4e2a\u65b0\u7684\u7528\u4e8e\u7f51\u7ea6\u8f66\u9700\u6c42\u9884\u6d4b\u7684\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u3002\u4f5c\u8005\u9996\u5148\u5c06\u533a\u57df\u95f4\u7684\u975e\u6b27\u76f8\u5173\u6027\u5efa\u6a21\u5230\u591a\u4e2a\u56fe\uff0c\u7136\u540e\u7528\u591a\u56fe\u5377\u79ef\uff08multi-graph convolution\uff09\u6765\u5efa\u6a21\u5176\u76f8\u5173\u6027\u3002\u7528\u5168\u5c40\u4e0a\u4e0b\u6587\u4fe1\u606f\uff08global contextual information\uff09\u6765\u5efa\u6a21\u65f6\u5e8f\u4fe1\u606f\uff0c\u5e76\u8fdb\u4e00\u6b65\u63d0\u51fa\u4e86\u4e0a\u4e0b\u6587\u95e8\u63a7\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u6a21\u578b\uff08contextual gated recurrent neural network\uff09\uff0c\u7ed9\u5386\u53f2\u6570\u636e\u5206\u914d\u6743\u91cd\u3002\u5728\u4e24\u4e2a\u6570\u636e\u96c6\u4e0a\u8868\u73b0\u5f3a\u4e8e\u5df2\u6709\u7b97\u6cd5\u7684 10% \u4ee5\u4e0a\u3002<\/span><\/p>\n

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\u636e Uber \u7edf\u8ba1\uff0c2018\u5e74\uff0c\u5168\u5e74\u7684\u7f51\u7ea6\u8f66\u4f7f\u7528\u6b21\u6570\u8fbe180\u4ebf\u6b21\uff0c\u8d85\u8fc7\u5168\u7403\u4eba\u53e3\u7684\u4e24\u500d\u3002\u51c6\u786e\u7684\u7f51\u7ea6\u8f66\u8ba2\u5355\u9884\u6d4b\uff0c\u80fd\u591f\u66f4\u597d\u7684\u8c03\u5ea6\u8f66\u8f86\uff0c\u63d0\u9ad8\u8f66\u8f86\u7684\u5229\u7528\u7387\uff0c\u7f13\u89e3\u4ea4\u901a\u62e5\u5835\uff0c\u5177\u6709\u91cd\u8981\u7684\u7ecf\u6d4e\u548c\u793e\u4f1a\u610f\u4e49\u3002<\/span><\/p>\n

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\u4f5c\u8005\u57fa\u4e8e\u5bf9\u7f51\u7ea6\u8f66\u9700\u6c42\u91cf\u9884\u6d4b\u95ee\u9898\u7684\u7406\u89e3\u4ee5\u53ca\u5148\u9a8c\u77e5\u8bc6\uff0c\u8bbe\u8ba1\u4e86\u5982\u4e0b\u7684\u7b97\u6cd5\u6846\u67b6\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n

<\/span><\/p>\n

\u4e3b\u8981\u5206\u4e3a\u4e09\u90e8\u5206\u3002<\/span><\/p>\n

<\/span><\/p>\n

\u5c06\u6bcf\u4e00\u5e27\u7684\u6570\u636e\u5efa\u6a21\u6210\u4e3a\u4e09\u5f20 graph\u3002\u6bcf\u5f20 graph \u4e0a\u7684\u8282\u70b9\u76f8\u540c\uff0c\u5373\u57ce\u5e02\u7684\u7f51\u683c\u5316\u5212\u5206\u3002\u8fde\u8fb9\u901a\u8fc7\u5982\u4e0b\u56fe\u6240\u793a\u7684\u89c4\u5219\u8fdb\u884c\u6784\u5efa\u3002\u6839\u636e\u4e0d\u540c\u7ef4\u5ea6\u7684\u76f8\u5173\u6027\uff08\u90bb\u57df\u4fe1\u606f\u3001\u529f\u80fd\u76f8\u4f3c\u5ea6\u3001\u4ea4\u901a\u8fde\u901a\u6027\uff09\u786e\u5b9a\u8282\u70b9\u4e4b\u95f4\u7684\u8fde\u8fb9\u503c\u3002<\/span><\/p>\n

<\/span><\/p>\n

\u65f6\u95f4\u7ef4\u5ea6\u7684\u9884\u6d4b\uff1a\u5c06T\u4e2a\u5386\u53f2\u65f6\u95f4\u6b65\u7684\u4fe1\u606f\u878d\u5408\u5230\u4e00\u5f20\u56fe\u4e0a\u3002<\/span><\/p>\n

<\/span><\/p>\n

\u7a7a\u95f4\u7ef4\u5ea6\u7684\u9884\u6d4b\uff1a\u5c06\u4e00\u4e2a\u65f6\u523b\u7684\u4e0d\u540c\u7684\u76f8\u5173\u6027\u7684\u56fe\u5408\u6210\u4e00\u4e2a\u56fe\u3002\u8fd9\u5c31\u662f\u8bba\u6587\u9898\u76ee\u4e2d\u6240\u8bf4\u7684\u591a\u56fe\u5377\u79ef\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n
\n
\n<\/p>\n
\n
\n
<\/p>\n

Contextual Gated Recurrent<\/strong><\/span><\/p>\n

Neural Network (CGRNN)<\/span><\/strong><\/span><\/p>\n

<\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

<\/span><\/h2>\n

\u8fd9\u662f\u7b97\u6cd5\u6846\u67b6\u4e2d\u7684\u7b2c\u4e8c\u6b65\uff0c\u5c06 T\u4e2a\u65f6\u95f4\u6b65\u7684\u5386\u53f2\u6570\u636e\u878d\u5408\u4e3a\u4e00\u5f20\u56fe\uff0c\u8fd9\u91cc\u6240\u8c13\u7684 Contextual Gated \u662f\u6307\u5c06\u5386\u53f2\u6570\u636e\u6bcf\u4e00\u5e27\u901a\u8fc7\u56fe\u5377\u79ef\u7f51\u7edc\u8fdb\u884c\u6570\u636e\u878d\u5408\uff0c\u5e76\u4e14\u901a\u8fc7\u5168\u5c40 pooling \u5f97\u5230\u8be5\u5c0f\u65f6\u7684\u5168\u5c40\u4fe1\u606f\u3002\u8fd9\u4e2a\u5168\u5c40\u4fe1\u606f\u518d\u4e0e\u539f\u6765\u7684 T\u4e2a\u65f6\u95f4\u6b65\u7684\u6570\u636e\u505a\u70b9\u4e58\uff0c\u5f97\u5230\u4e86\u5e26\u6743\u91cd\u7684\u5386\u53f2\u6570\u636e\u3002\u7136\u540e\u8fed\u4ee3\u5730\u8f93\u5165\u5230 RNN \u4e2d\u8fdb\u884c\u65f6\u95f4\u7ef4\u5ea6\u7684\u4fe1\u606f\u878d\u5408\uff0c\u6700\u7ec8\u5f97\u5230\u4e00\u5f20\u56fe\u3002\u8fd9\u91cc\u7684 RNN \u662f\u6743\u503c\u5171\u4eab\u7684\uff0c\u4e5f\u5c31\u662f\u5bf9\u4e8e\u56fe\u4e0a\u7684\u6bcf\u4e2a\u9876\u70b9\u90fd\u8fc7\u540c\u4e00\u4e2a RNN\uff0c\u5b83\u4eec\u7684\u6240\u8bad\u7ec3\u7684\u662f\u4e00\u5957\u53c2\u6570\uff08\u5de5\u7a0b\u4e0a\u662f\u53ef\u4ee5\u5b9e\u73b0\u7684\uff09\u3002\u8fd9\u6837\u7684\u597d\u5904\u6709\u4e24\u70b9\uff1a1. \u5b66\u4e60\u66f4\u52a0 general \u7684\u65f6\u5e8f\u7ef4\u5ea6\u7684\u6c47\u805a\u65b9\u6cd5\uff0c2. \u51cf\u5c11\u6a21\u578b\u590d\u6742\u7a0b\u5ea6\uff0c\u4f7f\u5f97\u66f4\u5bb9\u6613\u8bad\u7ec3\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n

\n

pooling \u64cd\u4f5c\u5982\u4e0b\u6240\u793a\uff0c\u5c06 t \u65f6\u523b graph \u4e0a\u6240\u6709\u8282\u70b9\u7684\u503c\u90fd\u52a0\u8d77\u6765\uff0c\u7136\u540e\u9664\u4ee5\u8282\u70b9\u7684\u4e2a\u6570\u3002<\/span><\/p>\n

<\/section>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

<\/section>\n

\u8fd9\u91cc\u9700\u8981\u6ce8\u610f\u7684\u70b9\u662f\uff0c\u6bcf\u4e00\u5e27\u6570\u636e\u505a\u56fe\u5377\u79ef\u7684\u65b9\u6cd5\u662f\u4f7f\u7528 k-ordered ChebNet\u3002<\/span>\u8fd9\u4e2a\u65b9\u6cd5\u4e0e\u6211\u4eec\u719f\u77e5\u7684\u56fe\u5377\u79ef\u7f51\u7edc GCN \u7684\u533a\u522b\u662f\uff0cGCN \u53ef\u4ee5\u7b80\u5355\u5730\u8ba4\u4e3a\u662f 1-ordered ChebNet\uff0c\u53ea\u662f\u6c47\u805a\u4e00\u9636\u90bb\u5c45\u3002<\/span>\u5982\u6587\u4e2d\u7ed9\u51fa\u7684\u793a\u610f\u56fe\u6240\u793a\u3002<\/span>\u9ed1\u8272\u662f\u4e2d\u5fc3\u8282\u70b9\uff0c\u9ec4\u8272\u662f 1\u9636\u90bb\u5c45\uff0c\u7ea2\u8272\u662f 2\u9636\u90bb\u5c45\u3002<\/span>\u901a\u8fc7 k-ordered ChebConv\uff0c\u53ef\u4ee5\u5b9e\u73b0 k \u9636\u9886\u57df\u7684\u4fe1\u606f\u4ea4\u4e92\u3002<\/span>\u4f7f\u7528 k \u5c42\u7684 GCN \u5806\u53e0\u4e5f\u53ef\u4ee5\u5b9e\u73b0 k \u9636\u90bb\u57df\u4fe1\u606f\u89d2\u5ea6\uff0c\u4f46\u662f\u968f\u7740\u5c42\u6570\u589e\u52a0\uff0c\u8bad\u7ec3\u96be\u5ea6\u52a0\u5927\u3002<\/span>\u6240\u4ee5\uff0c\u8fd9\u53ef\u80fd\u662f\u4f5c\u8005\u505a\u6a21\u578b\u9009\u62e9\u7684\u8003\u8651\u70b9\u4e4b\u4e00\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n

<\/span><\/p>\n

\u4e0b\u9762\u662f\u76f8\u5173\u7684\u8fd0\u7b97\u516c\u5f0f\uff1a<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

\"\u96c6\u667a\"<\/p>\n

\"\u96c6\u667a\"<\/p>\n

\n

\u5bf9 GCN \u4e5f\u4e0d\u592a\u719f\u6089\u7684\u540c\u5b66\u53ef\u4ee5\u8fd9\u6837\u901a\u4fd7\u7684\u7406\u89e3 GCN \u7684\u4f5c\u7528\u3002\u6bcf\u4e2a graph\uff0c\u90fd\u6709\u5bf9\u5e94\u7684\u8868\u793a\u8282\u70b9\u8fde\u63a5\u5173\u7cfb\u7684\u90bb\u63a5\u77e9\u9635\uff0c\u8fd8\u6709\u6570\u503c\u5728\u4e3b\u5bf9\u89d2\u7ebf\u4e0a\uff0c\u8868\u793a\u8282\u70b9\u4e00\u9636\u90bb\u5c45\u4e2a\u6570\u7684\u5ea6\u77e9\u9635\u3002\u62c9\u666e\u62c9\u65af\u77e9\u9635\u662f\u901a\u8fc7\u90bb\u63a5\u77e9\u9635\u3001\u5ea6\u77e9\u9635\uff0c\u7528\u4e0a\u9762\u56fe\u4e2d\u7684\u516c\u5f0f\u8ba1\u7b97\u5f97\u5230\u7684\u4e00\u4e2a\u65b9\u9635\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

<\/span><\/p>\n

\u5728\u56fe\u5377\u79ef\u4e2d\uff0c\u62c9\u666e\u62c9\u65af\u77e9\u9635\u4e2d\u7684\u5143\u7d20\uff0c\u7528\u6765\u5145\u5f53\u8282\u70b9\u4e4b\u95f4\u4fe1\u606f\u6c47\u805a\u65f6\uff0c\u90bb\u5c45\u8282\u70b9\u7684\u7279\u5f81\u5411\u91cf\u76f8\u52a0\u4e2d\u524d\u9762\u5bf9\u5e94\u7684\u6743\u91cd\u3002\u4e0b\u56fe\u662f\u7b97\u6cd5\u6846\u67b6\u4e2d\u7b2c\u4e09\u6b65\u7684\u5185\u5bb9\uff08\u8fd9\u4e24\u6b65\u90fd\u7528\u5230 k-ordered ChebConv\uff09\u3002\u5176\u4e2d\u4e0d\u8003\u8651\u591a\u56fe\u7684\u6c47\u805a\uff0c\u82b1\u62ec\u53f7\u4e2d\u7684\u5185\u5bb9\u5c31\u662f\u4e00\u4e2a\u56fe\u4e0a\u8fdb\u884c\u5377\u79ef\u7684\u64cd\u4f5c\u3002\u7b2c\u4e00\u4e2a\u65b9\u9635\u5c31\u662f\u62c9\u666e\u62c9\u65af\u77e9\u9635\uff0c\u7b2c\u4e8c\u4e2a\u77e9\u9635\u662f\u8fd9\u672c\u5377\u79ef\u5c42\u7684\u8f93\u5165\u7684\u90bb\u5c45\u8282\u70b9\u7684\u7279\u5f81\u5411\u91cf\uff08\u7ef4\u5ea6\u662f V*P\uff0cV \u662f\u8282\u70b9\u4e2a\u6570\uff0c\u7f16\u53f7\u7b49\u540c\u4e8e\u62c9\u666e\u62c9\u65af\u77e9\u9635\u4e2d\u8282\u70b9\u7f16\u53f7\uff0c\u4e5f\u5c31\u662f\u8282\u70b9\u5728\u884c\u5411\u91cf\u7684\u6392\u5e8f\uff0cP \u662f\u7279\u5f81\u5411\u91cf\u7ef4\u5ea6\uff09\u3002\u8fd9\u4e24\u4e2a\u77e9\u9635\u8fdb\u884c\u77e9\u9635\u4e58\u6cd5\uff0c\u5f97\u5230\u7684\u5c31\u662f\u6bcf\u4e2a\u8282\u70b9\u6c47\u805a\u4e86\u5176\u90bb\u5c45\u8282\u70b9\u7279\u5f81\u7684\u65b0\u7684\u7279\u5f81\u4fe1\u606f\u3002\u8fd9\u91cc\u7684\u90bb\u5c45\u8282\u70b9\u6839\u636e\u7b97\u6cd5\u7684 k-ordered \u6240\u5b9a\u4e49\u7684 k \u800c\u4e0d\u540c\uff0c\u800c\u8303\u56f4\u4e0d\u540c\uff0c\u4e14\u4f1a\u4f53\u73b0\u5230\u62c9\u666e\u62c9\u65af\u77e9\u9635\u4e0a\u3002\u7b2c\u4e09\u4e2a\u77e9\u9635 W \u8d77\u5230\u7684\u4f5c\u7528\u7b49\u4ef7\u4e8e MLP\uff0c\u505a\u7279\u5f81\u5411\u91cf\u7ef4\u5ea6\u7684\u53d8\u6362\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

<\/span><\/p>\n

\u611f\u5174\u8da3\u7684\u540c\u5b66\u53ef\u4ee5\u9605\u8bfb\u4e0b\u9762\u8d44\u6599\u83b7\u53d6\u66f4\u8be6\u7ec6\u7684\u4fe1\u606f\uff1aGCN \u4f5c\u8005 Kipf \u7684blog Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering\uff0c\u56de\u987e\u9891\u8c31\u56fe\u5377\u79ef\u7684\u7ecf\u5178\u2f2f\u5de5\u4f5c: \u4eceChebNet\u5230GCN\uff0c\u62c9\u666e\u62c9\u65af\u77e9\u9635\uff0c<\/span><\/p>\n

\n
\n<\/p>\n
\n
\n
<\/p>\n

Multi-Graph Conv<\/span><\/strong><\/span><\/p>\n

<\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

<\/span><\/h2>\n

\u7b97\u6cd5\u7684\u7b2c\u4e09\u6b65\u6240\u8fdb\u884c\u7684\u662f\u591a\u56fe\u7684\u4fe1\u606f\u6c47\u805a\u3002\u4e0b\u56fe\u662f\u4ee5\u5355\u4e2a\u8282\u70b9\u7684\u89c6\u89d2\u6765\u770b\u8fd9\u4e2a\u7b97\u6cd5\u7684\u8fd0\u884c\u3002\u9996\u5148\uff0c\u6bcf\u4e2a\u8282\u70b9\u5148\u5404\u81ea\u901a\u8fc7 k-ordered ChebConv\uff0c\u7136\u540e\u5728\u8fdb\u884c\u591a\u56fe\u6c47\u805a\uff0c\u6c47\u805a\u64cd\u4f5c\u7684\u5355\u4f4d\u662f\u8282\u70b9\u3002\u5728\u8fd9\u91cc\u662f\u5c06\u524d\u9762\u5206\u522b\u5efa\u6a21\u4e86\u90bb\u5c45\u4fe1\u606f\u3001\u529f\u80fd\u76f8\u4f3c\u5ea6\u3001\u4ea4\u901a\u8fde\u901a\u6027\u7684\u4e09\u4e2a\u56fe\u8fdb\u884c\u6c47\u805a\u3002<\/span><\/p>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n

<\/span><\/p>\n

\u4e0b\u56fe\u662f\u5177\u4f53\u7684\u591a\u56fe\u6c47\u805a\u7684\u516c\u5f0f\uff0c\u62ec\u53f7\u5185\u5c31\u662f\u666e\u901a\u7684 k\u9636 ChebConv\uff0c\u6c47\u805a\u7684\u65b9\u6cd5\u53ef\u4ee5\u662f\u52a0\u548c\u3001\u5e73\u5747\u3001\u57fa\u4e8e attention \u7684\u6c47\u805a\u7b49\u3002<\/span><\/p>\n

\n

\"\u96c6\u667a\"<\/section>\n

\n

\"\u96c6\u667a\"<\/section>\n
\n
\n<\/p>\n
\n
\n
<\/p>\n

\u5b9e\u9a8c<\/span><\/strong><\/span><\/p>\n

<\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

<\/span><\/h2>\n

\u6a21\u578b\u57fa\u4e8e\u6ef4\u6ef4\u6253\u8f66\u5728\u5317\u4eac\u548c\u4e0a\u6d77\u4e24\u4e2a\u57ce\u5e02\u7684\u5386\u53f2\u8ba2\u5355\u6570\u636e\u8fdb\u884c\u5b9e\u9a8c\u3002<\/span>\u5bf9\u6bd4\u4e86\u4e00\u4e9b\u57fa\u672c\u7684\u65f6\u5e8f\u9884\u6d4b\uff08\u5982\u679c\u4e0d\u8003\u8651\u56fe\u4e0a\u8282\u70b9\u4e4b\u95f4\u7684\u7a7a\u95f4\u76f8\u5173\u6027\uff0c\u53ef\u4ee5\u770b\u6210\u65f6\u5e8f\u9884\u6d4b\u95ee\u9898\uff09\u6a21\u578b\uff0c\u57fa\u4e8e image \u5efa\u6a21\u7684\u7b97\u6cd5\uff0c\u57fa\u4e8e graph \u7684\u7b97\u6cd5\u7b49\u3002<\/span>\u5747\u5f97\u5230\u4e86 10% \u7684\u63d0\u5347\u3002<\/span>\u5e76\u4e14\u8fdb\u4e00\u6b65\u8bd5\u9a8c\u4e86\u53bb\u6389 multi-graph \u4e2d\u7684\u67d0\u4e2a\u56fe\uff0c\u4ee5\u53ca\u66ff\u6362\u65f6\u95f4\u7ef4\u5ea6\u4fe1\u606f\u7684\u6c47\u805a\u65b9\u6cd5\uff0c\u5747\u8bc1\u660e\u5f53\u524d\u7684\u6a21\u578b\u662f\u6700\u597d\u7684\u3002<\/span><\/span><\/h2>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/section>\n
\"\u96c6\u667a\"<\/section>\n

<\/span><\/p>\n

\"\u96c6\u667a\"<\/p>\n

\n

\"\u96c6\u667a\"<\/p>\n

\n
\n
\n
\n
<\/p>\n

\u603b\u7ed3<\/span><\/strong><\/span><\/p>\n

<\/strong><\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

<\/span><\/p>\n

\u4f5c\u8005\u63d0\u5230\uff0c\u63a5\u4e0b\u6765\u4f1a\u5c06\u7b97\u6cd5\u63a8\u5e7f\u5230\u5176\u4ed6\u7684\u65f6\u7a7a\u6570\u636e\u9884\u6d4b\u95ee\u9898\u4e0a\uff0c\u6bd4\u5982\u4ea4\u901a\u6d41\u3001\u4eba\u6d41\u3001\u96fe\u973e\u9884\u62a5\u7b49\uff0c\u4ee5\u53ca\u63d0\u9ad8\u6a21\u578b\u7684\u80fd\u529b\uff0c\u4ee5\u6ee1\u8db3\u591a\u4e2a\u65f6\u95f4\u6b65\u7684\u9884\u6d4b\u9700\u6c42\u3002<\/span><\/h2>\n

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\u8bfb\u4e66\u4f1a\u89c6\u9891\u5206\u4eab\u94fe\u63a5\uff1a<\/span><\/section>\n
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\u2022<\/span>\u4f5c\u8005\u4e2a\u4eba\u4e3b\u9875\uff1a<\/span><\/p>\n

http:\/\/www-scf.usc.edu\/~yaguang<\/span><\/p>\n

\u2022\u8bba\u6587\u5730\u5740\uff1a<\/span><\/p>\n

http:\/\/www-scf.usc.edu\/~yaguang\/papers<\/span><\/p>\n

\/aaai19_multi<\/span>_graph_<\/span>convolution.pdf<\/span><\/p>\n

\u2022 slides\uff1a<\/span><\/p>\n

http:\/\/www-scf.usc.edu\/~yaguang\/papers<\/span><\/p>\n

\/aaai19_multi<\/span>_graph_<\/span>convolution_slides.pdf<\/span><\/p>\n

\u2022 poster\uff1a<\/span><\/p>\n

http:\/\/www-scf.usc.edu\/~yaguang\/papers<\/span><\/p>\n

\/aaai19_<\/span>stmgcn_<\/span>poster.pdf<\/span><\/p>\n

\u2022echarts\u683c\u70b9\u5730\u56fe<\/span><\/p>\n

https:\/\/echarts.baidu.com\/examples<\/span><\/section>\n
\/editor.<\/span>html?c=map-bin<\/span><\/span><\/section>\n

\u2022\u90d1\u5b87\uff1a\u6df1\u5ea6\u5b66\u4e60\u5728\u65f6\u7a7a\u6570\u636e\u4e2d\u7684\u5e94\u7528<\/span><\/p>\n

https:\/\/mp.weixin.qq.com\/s\/GakpFYtiNrVMNjPXUVjn1A<\/span><\/p>\n

\u2022Junbo Zhang, Yu Zheng, Dekang Qi. AAAI 2017. Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction<\/span><\/p>\n

https:\/\/arxiv.org\/abs\/1610.00081<\/span><\/p>\n

\u2022\u56de\u987e\u9891\u8c31\u56fe\u5377\u79ef\u7684\u7ecf\u5178\u5de5\u4f5c:<\/span><\/p>\n

\u4eceChebNet\u5230GCN\uff1a<\/span><\/p>\n

https:\/\/www.bilibili.com\/read\/mobile\/2855698<\/span><\/p>\n

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\u5229\u7528\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u8fdb\u884c\u624b\u52bf\u8bc6\u522b | \u8d75\u6587\u548c<\/span><\/strong><\/span><\/strong><\/a><\/p>\n

\u6df1\u5ea6\u5b66\u4e60\u65b0\u661f | \u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff08GCN\uff09\u6709\u591a\u5f3a\u5927\uff1f<\/span><\/strong><\/span><\/strong><\/a><\/p>\n

Nature | \u81ea\u52a8\u9a7e\u9a76\u8c03\u5ea6\u7b97\u6cd5\u6709\u671b\u5c06\u51fa\u79df\u8f66\u6570\u91cf\u51cf\u5c11\u4e00\u534a\uff08\u5185\u9644\u8bba\u6587\u539f\u6587\uff09<\/span><\/strong><\/span><\/strong><\/a><\/p>\n

\u5171\u4eab\u6c7d\u8f66\u5339\u914d\u7b97\u6cd5\u5982\u4f55\u5bfc\u81f4\u53f8\u6536\u5165\u7684\u5de8\u5927\u5dee\u5f02 | \u7f51\u7edc\u79d1\u5b66\u8bba\u6587\u901f\u901212\u7bc7<\/span><\/strong><\/span><\/strong><\/a><\/p>\n

\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/a><\/strong><\/p>\n

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\u5546\u52a1\u5408\u4f5c\u53ca\u6295\u7a3f\u8f6c\u8f7d\uff5cswarma@swarma.org<\/span><\/strong><\/p>\n

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\u25c6<\/span>\u25c6<\/span>\u25c6<\/span><\/span><\/strong><\/strong><\/h1>\n<\/section>\n

\u641c\u7d22\u516c\u4f17\u53f7\uff1a\u96c6\u667a\u4ff1\u4e50\u90e8<\/span><\/strong><\/span><\/p>\n

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\u8ba9\u82f9\u679c\u7838\u5f97\u66f4\u731b\u70c8\u4e9b\u5427\uff01<\/span><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u5bfc\u8bed \u636e Uber \u7edf\u8ba1\uff0c2018\u5e74\uff0c\u5168\u5e74\u7684\u7f51\u7ea6\u8f66\u4f7f\u7528\u6b21\u6570\u8fbe180\u4ebf\u6b21\uff0c\u8d85\u8fc7\u5168\u7403\u4eba\u53e3\u7684\u4e24\u500d\u3002\u51c6\u786e\u7684\u7f51\u7ea6\u8f66\u8ba2\u5355\u9884\u6d4b\uff0c\u80fd\u591f\u66f4\u597d\u7684\u8c03\u5ea6\u8f66\u8f86\uff0c\u63d0\u9ad8\u8f66\u8f86\u7684\u5229\u7528\u7387\uff0c\u51cf\u5c11\u7b49\u5f85\u65f6\u95f4\uff0c\u4ece\u800c\u7f13\u89e3\u4ea4\u901a\u62e5\u5835\uff0c\u5177\u6709\u91cd\u8981\u7684\u7ecf\u6d4e\u548c\u793e\u4f1a\u610f\u4e49\u3002\u8fd1\u5e74\u6765\u5927\u706b\u7684\u56fe\u795e\u7ecf\u7f51\u7edc GNN \u76f8\u5f53\u4e8e\u63d0\u4f9b\u4e86\u4e00\u79cd\u65b0\u7684\u5efa\u6a21\u548c\u89e3\u51b3\u65f6\u7a7a\u6570\u636e\u9884\u6d4b\u7684\u624b\u6bb5\u3002 ST-…<\/p>\n","protected":false},"author":1,"featured_media":16633,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[1],"tags":[],"special":[],"_links":{"self":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/16631"}],"collection":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=16631"}],"version-history":[{"count":0,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/posts\/16631\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=\/wp\/v2\/media\/16633"}],"wp:attachment":[{"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=16631"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=16631"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=16631"},{"taxonomy":"special","embeddable":true,"href":"https:\/\/swarma.org\/index.php?rest_route=%2Fwp%2Fv2%2Fspecial&post=16631"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}