{"id":45760,"date":"2023-10-13T16:43:42","date_gmt":"2023-10-13T08:43:42","guid":{"rendered":"https:\/\/swarma.org\/?p=45760"},"modified":"2023-10-13T16:43:42","modified_gmt":"2023-10-13T08:43:42","slug":"%e5%9b%be%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e6%a6%82%e8%bf%b0","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=45760","title":{"rendered":"\u56fe\u795e\u7ecf\u7f51\u7edc\u6982\u8ff0"},"content":{"rendered":"
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LK <\/span>| \u4f5c\u8005<\/span><\/strong><\/span><\/strong><\/section>\n
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\u8fd1\u671fAI\u9886\u57df\u6700\u70ed\u95e8\u7684\u65b9\u5411\uff0cAI for science\u4e2d\uff0c\u6709\u8bb8\u591a\u7814\u7a76\u5de5\u4f5c\uff0c\u90fd\u7528\u5230\u4e86\u56fe\u795e\u7ecf\u7f51\u7edc\u3002\u672c\u6587\u7b80\u8981\u4ecb\u7ecd\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u6982\u5ff5\u4e0e\u5206\u7c7b\uff0c\u6700\u540e\u4ecb\u7ecd\u4e00\u4e2a\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u5728AI for science\u4e2d\u5178\u578b\u5e94\u7528\u3002
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\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u9886\u57df\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u6210\u529f\u3002\u5e38\u7528\u7684\u6df1\u5ea6\u795e\u7ecf\u7f51\u7edc\uff0c\u5982\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\uff0c\u5728\u5904\u7406\u6b27\u5f0f\u7ed3\u6784\u7684\u6570\u636e\u65b9\u9762\u8868\u73b0\u51fa\u8272\u3002\u7136\u800c\uff0c\u5bf9\u4e8e\u56fe\u7ed3\u6784\u7684\u6570\u636e\uff0c\u5982\u793e\u4ea4\u7f51\u7edc\u4e2d\u7684\u4eba\u9645\u5173\u7cfb\u3001\u5316\u5b66\u4e2d\u7684\u5206\u5b50\u76f8\u4e92\u4f5c\u7528\u3001\u4e09\u7ef4\u70b9\u4e91\u6570\u636e\u4ee5\u53ca\u6570\u636e\u4e2d\u5404\u4e2a\u5355\u5143\u4e4b\u95f4\u7684\u7ed3\u6784\uff0c\u60c5\u51b5\u5c31\u53d8\u5f97\u590d\u6742\u4e86\u3002\u8fd9\u4e9b\u6570\u636e\u4e0d\u518d\u6309\u7167\u6b27\u5f0f\u7a7a\u95f4\u4e2d\u7684\u6709\u5e8f\u6392\u5217\uff0c\u800c\u662f\u5448\u73b0\u51fa\u65e0\u5e8f\u7684\u5206\u5e03\u3002\u5728\u8fd9\u79cd\u60c5\u51b5\u4e0b\uff0c\u4f20\u7edf\u7684\u795e\u7ecf\u7f51\u7edc\u7b97\u5b50\u4e0d\u518d\u9002\u7528\u3002<\/span><\/o:p><\/p>\n

\u8fd1\u5e74\u6765\uff0c\u7814\u7a76\u4eba\u5458\u501f\u9274\u4e86\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u3001\u5faa\u73af\u795e\u7ecf\u7f51\u7edc\u548c\u6df1\u5ea6\u81ea\u7f16\u7801\u5668\u7684\u601d\u8def\uff0c\u8bbe\u8ba1\u4e86\u4e13\u95e8\u7528\u4e8e\u5904\u7406\u56fe\u7ed3\u6784\u6570\u636e\u7684\u795e\u7ecf\u7f51\u7edc\uff0c\u79f0\u4e4b\u4e3a\u56fe\u795e\u7ecf\u7f51\u7edc<\/span>\uff08Graph Neural Network\uff0c\u7b80\u79f0GNN\uff09<\/span>\u3002\u8fd9\u4e00\u521b\u65b0\u4f7f\u5f97\u795e\u7ecf\u7f51\u7edc\u8fd9\u4e00\u5f3a\u5927\u5de5\u5177\u80fd\u591f\u6709\u6548\u5730\u5e94\u7528\u4e8e\u5904\u7406\u5404\u79cd\u56fe\u7ed3\u6784\u7684\u6570\u636e\u3002<\/span><\/p>\n

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\u56fe1 \u6b27\u5f0f\u6570\u636e\u7ed3\u6784\u4e0e\u56fe\u6570\u636e\u7ed3\u6784[2].\u5de6\u56fe\u4e2d\u7684\u56fe\u50cf\uff0c\u5404\u4e2a\u50cf\u7d20\u5448\u89c4\u5219\u7684\u6b27\u5f0f\u6392\u5e03\uff0c\u53f3\u56fe\u4e2d\u7684\u84dd\u8272\u8282\u70b9\uff0c\u76f8\u4e92\u4e4b\u95f4\u5448\u968f\u673a\u5173\u8054\uff0c\u7528\u56fe\u63cf\u8ff0\u3002\u4e24\u79cd\u4e0d\u540c\u7ed3\u6784\u7684\u6570\u636e\uff0c\u9700\u8981\u4f7f\u7528\u4e0d\u540c\u7c7b\u578b\u7684\u795e\u7ecf\u7f51\u7edc\u5904\u7406\u3002<\/span><\/o:p><\/section>\n

\u56fe\u6570\u636e\u662f\u7531\u8282\u70b9<\/span>\uff08nodes\uff09<\/span>\u548c\u8fb9<\/span>\uff08edges\uff09<\/span>\u7ec4\u6210\u7684\u6570\u636e\u7ed3\u6784\uff0c\u901a\u5e38\u7528\u4e8e\u8868\u793a\u5404\u79cd\u5173\u7cfb\u548c\u7f51\u7edc\uff0c\u5982\u793e\u4ea4\u7f51\u7edc\u3001\u77e5\u8bc6\u56fe\u8c31\u3001\u5206\u5b50\u7ed3\u6784\u3001\u4ea4\u901a\u7f51\u7edc\u7b49\u3002GNNs\u7684\u76ee\u6807\u662f\u4ece\u8fd9\u4e9b\u56fe\u6570\u636e\u4e2d\u5b66\u4e60\u6709\u5173\u8282\u70b9\u548c\u8fb9\u7684\u4fe1\u606f\uff0c\u4ee5\u4fbf\u8fdb\u884c\u5404\u79cd\u4efb\u52a1\uff0c\u4f8b\u5982\u8282\u70b9\u5206\u7c7b\u3001\u56fe\u5206\u7c7b\u3001\u94fe\u63a5\u9884\u6d4b\u7b49\u3002<\/span><\/o:p><\/p>\n

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1.Recurrent graph neural networks (RecGNNs) \u9012\u5f52\u56fe\u795e\u7ecf\u7f51\u7edc<\/span><\/strong><\/span><\/o:p><\/p>\n

RecGNNs\u65e8\u5728\u4f7f\u7528\u9012\u5f52\u795e\u7ecf\u7ed3\u6784\u5b66\u4e60\u8282\u70b9\u8868\u793a\u3002\u5b83\u4eec\u5047\u8bbe\u56fe\u4e2d\u7684\u4e00\u4e2a\u8282\u70b9\u4e0d\u65ad\u4e0e\u5176\u90bb\u5c45\u8fdb\u884c\u4fe1\u606f\/\u6d88\u606f\u4ea4\u6362\uff0c\u76f4\u5230\u8fbe\u5230\u7a33\u5b9a\u7684\u5e73\u8861\u72b6\u6001\u3002RecGNNs\u5728\u6982\u5ff5\u4e0a\u975e\u5e38\u91cd\u8981\uff0c\u5bf9\u540e\u7eed\u5bf9\u5377\u79ef\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u7814\u7a76\u6709\u4e00\u5b9a\u7684\u542f\u53d1\u4f5c\u7528\u3002\u540e\u6765\u7684\u5377\u79ef\u56fe\u795e\u7ecf\u7f51\u7edc\u7ee7\u627f\u4e86\u5176\u4e2d\u7684\u6d88\u606f\u4f20\u9012\u7684\u7406\u5ff5\u3002<\/span><\/o:p><\/p>\n

2.Convolutional graph neural networks (ConvGNNs) \u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc<\/span><\/strong><\/span><\/o:p><\/p>\n

\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5c06\u5377\u79ef\u7684\u6982\u5ff5\u4ece\u6b27\u5f0f\u7ed3\u6784\u7684\u6570\u636e\u6269\u5c55\u5230\u56fe\u7ed3\u6784\u7684\u6570\u636e\u3002\u56fe\u5377\u79ef\u7684\u57fa\u672c\u601d\u8def\u662f\u805a\u5408\u4e00\u4e2a\u8282\u70b9\u5468\u56f4\u8282\u70b9\u7684\u7279\u5f81\uff0c\u7528\u4ee5\u751f\u6210\u8be5\u8282\u70b9\u81ea\u8eab\u7684\u7279\u5f81\u3002\u56fe\u5377\u79ef\u7f51\u7edc\u5806\u53e0\u4e86\u591a\u4e2a\u201c\u56fe\u5377\u79ef\u5c42\u201d\uff0c\u7528\u6765\u63d0\u53d6\u5404\u4e2a\u8282\u70b9\u7684\u9ad8\u5c42\u6b21\u4fe1\u606f\u3002\u56fe\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u5177\u4f53\u5206\u4e3a\u8c31\u65b9\u6cd5<\/span>\uff08Spectral methods\uff09<\/span>\u548c\u7a7a\u95f4\u65b9\u6cd5<\/span>\uff08Spatial methods\uff09<\/span>\u3002\u8c31\u65b9\u6cd5\u7684\u5377\u79ef\u64cd\u4f5c\u4f9d\u8d56\u4e8e\u56fe\u7684\u62c9\u666e\u62c9\u65af\u77e9\u9635\u7684\u7279\u5f81\u503c\u548c\u7279\u5f81\u5411\u91cf\u3002\u7a7a\u95f4\u65b9\u6cd5\u7684\u5377\u79ef\u5219\u76f4\u63a5\u5728\u56fe\u7684\u90bb\u57df\u4e0a\u8fdb\u884c\u3002<\/span><\/o:p><\/p>\n

3.Graph autoencoders (GAEs) \u56fe\u81ea\u7f16\u7801\u5668<\/span><\/strong><\/span><\/o:p><\/p>\n

\u56fe\u81ea\u7f16\u7801\u5668\u662f\u4e00\u79cd\u65e0\u76d1\u7763\u7684\u5b66\u4e60\u65b9\u5f0f\u3002\u5b83\u5c06\u56fe\u7f16\u7801\u6210\u4e00\u4e2a\u9690\u5f0f\u5411\u91cf\uff0c\u901a\u8fc7\u7f16\u7801\u4fe1\u606f\u6765\u91cd\u5efa\u56fe\u4e2d\u7684\u6570\u636e\u3002\u56fe\u81ea\u7f16\u7801\u5668\u7528\u4e8e\u5b66\u4e60\u7f51\u7edc\u4e2d\u7684\u5d4c\u5165\uff0c\u4ee5\u53ca\u751f\u6210\u56fe\u7684\u751f\u6210\u5f0f\u5206\u5e03\u3002\u56fe\u81ea\u7f16\u7801\u5668\u88ab\u5e7f\u6cdb\u5e94\u7528\u4e8e\u793e\u4ea4\u7f51\u7edc\u5206\u6790\u3001\u751f\u7269\u4fe1\u606f\u5b66\u548c\u63a8\u8350\u7cfb\u7edf\u4e2d\u3002\u901a\u8fc7\u5b66\u4e60\u6709\u6548\u7684\u56fe\u8868\u793a\uff0c\u56fe\u81ea\u7f16\u7801\u5668\u6709\u52a9\u4e8e\u5bf9\u590d\u6742\u5173\u7cfb\u6570\u636e\u7684\u7406\u89e3\u548c\u5e94\u7528\u3002<\/span><\/o:p><\/p>\n

4.Spatial-temporal graph neural networks (STGNNs) \u65f6\u7a7a\u56fe\u795e\u7ecf\u7f51\u7edc<\/span><\/strong><\/span><\/o:p><\/p>\n

\u65f6\u7a7a\u56fe\u795e\u7ecf\u7f51\u7edc\u7528\u4e8e\u4ece\u65f6\u7a7a\u56fe\u4e2d\uff0c\u5b66\u4e60\u9690\u85cf\u7684\u6a21\u5f0f\u3002\u65f6\u7a7a\u6570\u636e\u901a\u5e38\u662f\u6307\u5177\u6709\u65f6\u95f4\u548c\u7a7a\u95f4\u7ef4\u5ea6\u7684\u6570\u636e\uff0c\u4f8b\u5982\u79fb\u52a8\u7269\u4f53\u7684\u8f68\u8ff9\u3001\u6c14\u8c61\u6570\u636e\u3001\u4ea4\u901a\u6d41\u91cf\u7b49\u3002\u8fd9\u4e9b\u6570\u636e\u901a\u5e38\u4ee5\u56fe\u7684\u5f62\u5f0f\u8868\u793a\uff0c\u5176\u4e2d\u8282\u70b9\u4ee3\u8868\u5730\u70b9\u6216\u5b9e\u4f53\uff0c\u8fb9\u4ee3\u8868\u65f6\u7a7a\u5173\u7cfb\u3002\u65f6\u7a7a\u56fe\u53ef\u4ee5\u662f\u6709\u5411\u7684\u6216\u65e0\u5411\u7684\uff0c\u5177\u4f53\u53d6\u51b3\u4e8e\u5e94\u7528\u573a\u666f\u3002ST-GNN\u7684\u5173\u952e\u4efb\u52a1\u662f\u5efa\u6a21\u65f6\u7a7a\u6570\u636e\u4e2d\u7684\u52a8\u6001\u5173\u7cfb\u3002\u5b83\u53ef\u4ee5\u901a\u8fc7\u4e0d\u540c\u7684\u65b9\u5f0f\u6765\u5904\u7406\u65f6\u7a7a\u6570\u636e\uff0c\u4f8b\u5982\u901a\u8fc7\u5bf9\u6bcf\u4e2a\u65f6\u523b\u7684\u5feb\u7167\u5efa\u7acb\u56fe\uff0c\u6216\u8005\u901a\u8fc7\u5728\u56fe\u4e0a\u5f15\u5165\u65f6\u95f4\u4fe1\u606f\u6765\u6355\u6349\u52a8\u6001\u6027\u3002\u8fd9\u4f7f\u5f97ST-GNN\u80fd\u591f\u5728\u65f6\u95f4\u4e0a\u8fdb\u884c\u63a8\u7406\u548c\u9884\u6d4b\u3002ST-GNN\u5b66\u4e60\u6bcf\u4e2a\u8282\u70b9\u7684\u8868\u793a\uff0c\u4f7f\u5f97\u8fd9\u4e9b\u8868\u793a\u5305\u542b\u4e86\u8282\u70b9\u7684\u5c5e\u6027\u4fe1\u606f\u4ee5\u53ca\u4e0e\u5176\u4ed6\u8282\u70b9\u7684\u65f6\u7a7a\u5173\u7cfb\u3002\u8fd9\u6709\u52a9\u4e8e\u5bf9\u8282\u70b9\u8fdb\u884c\u65f6\u7a7a\u4e0a\u7684\u9884\u6d4b\u6216\u5206\u7c7b\u3002ST-GNN\u5728\u8bb8\u591a\u5e94\u7528\u9886\u57df\u90fd\u6709\u5e7f\u6cdb\u7684\u5e94\u7528\uff0c\u5305\u62ec\u4ea4\u901a\u7ba1\u7406<\/span>\uff08\u4ea4\u901a\u6d41\u91cf\u9884\u6d4b\u3001\u8def\u5f84\u89c4\u5212\uff09<\/span>\u3001\u6c14\u8c61\u9884\u6d4b\u3001\u5730\u7406\u4fe1\u606f\u7cfb\u7edf<\/span>\uff08GIS\uff09<\/span>\u3001\u793e\u4ea4\u7f51\u7edc\u5206\u6790\u7b49\u3002\u5728\u8fd9\u4e9b\u9886\u57df\u4e2d\uff0cST-GNN\u53ef\u4ee5\u5e2e\u52a9\u7406\u89e3\u65f6\u7a7a\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u3001\u8d8b\u52bf\u548c\u5f02\u5e38\u3002<\/span><\/o:p><\/p>\n

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\u8282\u70b9\u8868\u793a\uff08Node Embeddings\uff09<\/span><\/strong>\uff1aGNN\u7684\u4e3b\u8981\u4efb\u52a1\u4e4b\u4e00\u662f\u5b66\u4e60\u6bcf\u4e2a\u8282\u70b9\u7684\u8868\u793a<\/span>\uff08\u6216\u79f0\u5d4c\u5165\uff09<\/span>\u3002\u8fd9\u4e9b\u8868\u793a\u901a\u5e38\u662f\u4e00\u4e2a\u56fa\u5b9a\u7ef4\u5ea6\u7684\u5411\u91cf\uff0c\u7528\u4e8e\u6355\u6349\u8282\u70b9\u7684\u7279\u5f81\u3002GNN\u901a\u8fc7\u805a\u5408\u8282\u70b9\u7684\u90bb\u5c45\u8282\u70b9\u4fe1\u606f\u6765\u66f4\u65b0\u8282\u70b9\u8868\u793a\u3002\u8fd9\u4e2a\u8fc7\u7a0b\u5728\u6bcf\u4e2a\u56fe\u5377\u79ef\u5c42\u4e2d\u8fed\u4ee3\u8fdb\u884c\u3002<\/span><\/o:p><\/p>\n

\u56fe\u5377\u79ef\uff08Graph Convolution\uff09\uff1a<\/span><\/strong>\u56fe\u5377\u79ef\u662fGNN\u4e2d\u7684\u6838\u5fc3\u64cd\u4f5c\u3002\u5728\u6bcf\u4e00\u5c42\uff0c\u5b83\u4f1a\u8003\u8651\u8282\u70b9\u7684\u90bb\u5c45\u4fe1\u606f\uff0c\u5e76\u4f7f\u7528\u6743\u91cd\u6765\u805a\u5408\u8fd9\u4e9b\u4fe1\u606f\u4ee5\u66f4\u65b0\u8282\u70b9\u7684\u8868\u793a\u3002\u8fd9\u4f7f\u5f97\u8282\u70b9\u80fd\u591f\u6355\u6349\u5230\u5468\u56f4\u8282\u70b9\u7684\u4fe1\u606f\uff0c\u4ece\u800c\u6539\u5584\u5bf9\u8282\u70b9\u7684\u5206\u7c7b\u6216\u9884\u6d4b\u4efb\u52a1\u3002<\/span><\/o:p><\/p>\n

\u805a\u5408\u51fd\u6570\uff08Aggregation Function\uff09\uff1a<\/span><\/strong>\u5728\u56fe\u5377\u79ef\u4e2d\uff0c\u8282\u70b9\u90bb\u5c45\u7684\u4fe1\u606f\u88ab\u805a\u5408\u6210\u4e00\u4e2a\u65b0\u7684\u8282\u70b9\u8868\u793a\u3002\u5e38\u89c1\u7684\u805a\u5408\u51fd\u6570\u5305\u62ec\u6c42\u548c\u3001\u5e73\u5747\u503c\u3001\u6700\u5927\u503c\u7b49\u3002\u4e0d\u540c\u7684\u805a\u5408\u51fd\u6570\u53ef\u4ee5\u7528\u4e8e\u4e0d\u540c\u7684\u4efb\u52a1\u548c\u56fe\u7ed3\u6784\u3002<\/span><\/o:p><\/p>\n

\u56fe\u6c60\u5316\uff08Graph Pooling\uff09\uff1a<\/span><\/strong>\u7c7b\u4f3c\u4e8e\u5377\u79ef\u795e\u7ecf\u7f51\u7edc\u4e2d\u7684\u6c60\u5316\u64cd\u4f5c\uff0c\u56fe\u6c60\u5316\u7528\u4e8e\u51cf\u5c0f\u56fe\u7684\u89c4\u6a21\u548c\u590d\u6742\u6027\u3002\u5b83\u53ef\u4ee5\u901a\u8fc7\u5408\u5e76\u8282\u70b9\u6216\u8fb9\u6765\u51cf\u5c0f\u56fe\u7684\u5927\u5c0f\uff0c\u4ee5\u4fbf\u5728\u540e\u7eed\u5c42\u4e2d\u5904\u7406\u66f4\u62bd\u8c61\u7684\u4fe1\u606f\u3002<\/span><\/o:p><\/p>\n

\u4efb\u52a1\u5c42\uff08Task Layer\uff09\uff1a<\/span><\/strong>GNN\u901a\u5e38\u6700\u540e\u4f1a\u8fde\u63a5\u4e00\u4e2a\u4efb\u52a1\u5c42\uff0c\u7528\u4e8e\u6267\u884c\u5177\u4f53\u7684\u4efb\u52a1\uff0c\u5982\u8282\u70b9\u5206\u7c7b\u3001\u56fe\u5206\u7c7b\u3001\u94fe\u63a5\u9884\u6d4b\u7b49\u3002\u4efb\u52a1\u5c42\u7684\u7ed3\u6784\u53d6\u51b3\u4e8e\u6240\u89e3\u51b3\u7684\u4efb\u52a1\u7c7b\u578b\u3002<\/span><\/o:p><\/p>\n

\u53cd\u5411\u4f20\u64ad\uff08Backpropagation\uff09\uff1a<\/span><\/strong>\u4e0e\u5176\u4ed6\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u4e00\u6837\uff0cGNN\u4f7f\u7528\u53cd\u5411\u4f20\u64ad\u7b97\u6cd5\u6765\u66f4\u65b0\u6a21\u578b\u53c2\u6570\uff0c\u4ee5\u6700\u5c0f\u5316\u635f\u5931\u51fd\u6570\u3002\u8fd9\u4f7f\u5f97\u6a21\u578b\u80fd\u591f\u4ece\u8bad\u7ec3\u6570\u636e\u4e2d\u5b66\u4e60\uff0c\u5e76\u63d0\u9ad8\u5176\u6027\u80fd\u3002<\/span><\/o:p><\/p>\n

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\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u4e00\u4e2a\u91cd\u8981\u5e94\u7528\u662f\u5bf9\u7269\u7406\u4e16\u754c\u8fdb\u884c\u5efa\u6a21\u3002\u5c06\u7269\u7406\u4e16\u754c\u4e2d\uff0c\u4e2a\u4f53\u4e0e\u4e2a\u4f53\u4e4b\u95f4\u7684\u5173\u7cfb\u8868\u793a\u4e3a\u56fe\uff0c\u53ef\u4ee5\u901a\u8fc7\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u5bf9\u73b0\u5b9e\u4e16\u754c\u4e2d\u590d\u6742\u7cfb\u7edf\u7684\u884c\u4e3a\uff0c\u8fdb\u884c\u63a8\u65ad\u3002<\/span><\/o:p><\/p>\n

\u4e0b\u9762\uff0c\u6211\u4eec\u4ee52022\u5e74\u53d1\u8868\u5728 PNAS\u4e0a\u7684\u4e00\u7bc7\u8bba\u6587 Learning physics-consistent particle interactions [4]\u4e3a\u4f8b\uff0c \u6765\u8bf4\u660e\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u662f\u5982\u4f55\u5efa\u6a21\u7269\u7406\u4e16\u754c\u7684\u3002<\/span><\/o:p><\/p>\n

\u8be5\u6587\u7528\u56fe\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\uff0c\u6a21\u62df\u7269\u7406\u4e16\u754c\u4e2d\uff0c\u7c92\u5b50\u4e4b\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\u3002\u73b0\u5b9e\u4e16\u754c\u4e2d\u7c92\u5b50\u7684\u76f8\u4e92\u4f5c\u7528\u5177\u6709\u9ad8\u5ea6\u590d\u6742\u6027\uff0c\u96be\u4ee5\u901a\u8fc7\u4eba\u4e3a\u8bbe\u8ba1\u7cbe\u786e\u5efa\u6a21\uff0c\u501f\u52a9\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u5229\u7528\u6570\u636e\u9a71\u52a8\u7684\u601d\u8def\uff0c\u53ef\u4ee5\u5bf9\u590d\u6742\u7684\u7269\u7406\u4e16\u754c\u8fdb\u884c\u51c6\u786e\u5efa\u6a21\u3002<\/span><\/o:p><\/p>\n

\u8be5\u6587\u63d0\u51fa\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u6846\u67b6\uff0c\u53ef\u4ee5\u51c6\u786e\u63a8\u65ad\u51fa\u7c92\u5b50\u4e4b\u95f4\u7684\u4f5c\u7528\u529b\u4e0e\u52bf\u80fd\uff0c\u4e14\u7c92\u5b50\u95f4\u7684\u76f8\u4e92\u4f5c\u7528\uff0c\u6ee1\u8db3\u725b\u987f\u7b2c\u4e8c\u5b9a\u5f8b\u7684\u7ea6\u675f\u3002\u73b0\u6709\u7684\u76f8\u5173\u5de5\u4f5c\uff0c\u867d\u7136\u4e5f\u5229\u7528\u4e86\u56fe\u795e\u7ecf\u7f51\u7edc\u7684\u6846\u67b6\uff0c\u4f46\u5927\u591a\u6ca1\u6709\u8003\u8651\u725b\u987f\u8fd0\u52a8\u5b9a\u5f8b\u7684\u7ea6\u675f\uff0c\u5bfc\u81f4\u7f51\u8def\u63a8\u65ad\u7684\u7ed3\u679c\uff0c\u4e0d\u7b26\u5408\u7269\u7406\u89c4\u5f8b\u3002<\/span><\/o:p><\/section>\n
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\u56fe \u795e\u7ecf\u7f51\u7edc\u5efa\u6a21\u7c92\u5b50\u8fd0\u52a8[4]<\/span><\/p>\n

\u8be5\u6587\u7684\u5de5\u4f5c\uff0c\u6210\u529f\u5c06\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u5e94\u7528\u5230\u7269\u7406\u4e16\u754c\u7684\u5efa\u6a21\u63a8\u65ad\u4e2d\uff0c\u5373\u5229\u7528\u4e86\u6570\u636e\u9a71\u52a8\u7684\u65b9\u6cd5\uff0c\u53c8\u6ee1\u8db3\u4e86\u7269\u7406\u89c4\u5f8b\u7684\u7ea6\u675f\uff0c\u503c\u5f97\u501f\u9274\u5b66\u4e60\u3002<\/span><\/p>\n

\u53c2\u8003\u6587\u732e<\/span><\/strong><\/span><\/section>\n
[1]Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE transactions on neural networks and learning systems, 32(1), 4-24.<\/span><\/o:p><\/section>\n
[2] Liu, Z., & Zhou, J. (2022). Introduction to graph neural networks. Springer Nature.<\/span><\/o:p><\/section>\n
[3] Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., … & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81.<\/span><\/o:p><\/section>\n
[4] Han, Z., Kammer, D. S., & Fink, O. (2022). Learning physics-consistent particle interactions. PNAS nexus, 1(5), pgac264.<\/span><\/o:p><\/section>\n
\u5c01\u9762\uff1aLiu, Z., & Zhou, J. (2022). Introduction to graph neural networks. Springer Nature.<\/span><\/section>\n


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1. <\/strong>Barab\u00e1si \u6700\u65b0\u7814\u7a76\uff1a\u5229\u7528\u56fe\u795e\u7ecf\u7f51\u7edc\u52a0\u901f\u7f51\u7edc\u5e03\u5c40<\/a><\/strong><\/span><\/strong><\/strong><\/section>\n
2. <\/strong>2023\u5e74\uff0c\u8bf7\u4e0d\u8981\u5ffd\u7565\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\uff08GNN\uff09\u8fd9\u4e00\u5339\u9ed1\u9a6c\uff01<\/a><\/strong><\/strong><\/span><\/strong><\/section>\n
3.<\/strong> Nat. Mach. Intell. \u901f\u9012\uff1a\u56fe\u795e\u7ecf\u7f51\u7edc\u5b9e\u73b0\u4e09\u7ef4\u6d41\u4f53\u8fd0\u52a8\u4e2d\u7684\u7c92\u5b50\u8ddf\u8e2a<\/a><\/strong><\/span><\/section>\n
4. <\/strong><\/strong><\/span>\u5f20\u6c5f\uff1a\u7b2c\u4e09\u4ee3\u4eba\u5de5\u667a\u80fd\u6280\u672f\u57fa\u7840\u2014\u2014\u4ece\u53ef\u5fae\u5206\u7f16\u7a0b\u5230\u56e0\u679c\u63a8\u7406 | \u96c6\u667a\u5b66\u56ed\u5168\u65b0\u8bfe\u7a0b<\/a><\/strong><\/strong><\/span><\/section>\n
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6. <\/strong>\u52a0\u5165\u96c6\u667a\uff0c\u4e00\u8d77\u590d\u6742\uff01<\/strong><\/span><\/a><\/section>\n


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