{"id":23931,"date":"2021-02-20T15:57:15","date_gmt":"2021-02-20T07:57:15","guid":{"rendered":"https:\/\/swarma.org\/?p=23931"},"modified":"2021-02-20T15:57:15","modified_gmt":"2021-02-20T07:57:15","slug":"aaai2021-%e5%9b%be%e7%a5%9e%e7%bb%8f%e7%bd%91%e7%bb%9c%e7%a0%94%e7%a9%b6%e8%bf%9b%e5%b1%95%e8%a7%a3%e8%af%bb","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=23931","title":{"rendered":"AAAI2021 | \u56fe\u795e\u7ecf\u7f51\u7edc\u7814\u7a76\u8fdb\u5c55\u89e3\u8bfb"},"content":{"rendered":"
<\/p>\n

\u5bfc\u8bed<\/p>\n

AAAI \u7684\u82f1\u6587\u5168\u79f0\u662f Association for the Advance of Artificial Intelligence\u2014\u2014\u7f8e\u56fd\u4eba\u5de5\u667a\u80fd\u534f\u4f1a\u3002\u8be5\u534f\u4f1a\u662f\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u4e3b\u8981\u5b66\u672f\u7ec4\u7ec7\u4e4b\u4e00\uff0c\u5176\u4e3b\u529e\u7684\u5e74\u4f1a\u4e5f\u662f\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684\u56fd\u9645\u9876\u7ea7\u4f1a\u8bae\u3002\u5728\u4e2d\u56fd\u8ba1\u7b97\u673a\u5b66\u4f1a\u7684\u56fd\u9645\u5b66\u672f\u4f1a\u8bae\u6392\u540d\u4ee5\u53ca\u6e05\u534e\u5927\u5b66\u65b0\u53d1\u5e03\u7684\u8ba1\u7b97\u673a\u79d1\u5b66\u63a8\u8350\u5b66\u672f\u4f1a\u8bae\u548c\u671f\u520a\u5217\u8868\u4e2d\uff0cAAAI \u5747\u88ab\u5217\u4e3a\u4eba\u5de5\u667a\u80fd\u9886\u57df\u7684 A \u7c7b\u9876\u7ea7\u4f1a\u8bae\u3002
\u5317\u90ae GAMMA Lab | \u6765\u6e90


AAAI 2021\u8bba\u6587\u63a5\u6536\u5217\u8868\u5982\u4e0b\uff1a<\/p>\n

https:\/\/aaai.org\/Conferences\/AAAI-21\/wp-content\/uploads\/2020\/12\/AAAI-21_Accepted-Paper-List.Main_.Technical.Track_.pdf<\/p>\n

\u672c\u6587\u4e3b\u8981\u68b3\u7406\u4e86AAAI 2021\u4e0a\u56fe\u795e\u7ecf\u7f51\u7edc\u65b9\u9762\u7684\u6700\u65b0\u8fdb\u5c55\uff0c\u4e3b\u8981\u6db5\u76d6\uff1a
\u66f4\u52a0\u57fa\u7840\u7684\u7814\u7a76\uff1a\u8868\u793a\u80fd\u529b\/\u8fc7\u5e73\u6ed1\/\u4f20\u64ad\u673a\u5236\/\u707e\u96be\u6027\u9057\u5fd8\u66f4\u52a0\u590d\u6742\u7684\u56fe\u6570\u636e\uff1a\u5f02\u8d28\u56fe\/\u6709\u5411\u56fe\/\u52a8\u6001\u56fe\u66f4\u52a0\u4e30\u5bcc\u7684\u8bad\u7ec3\u7b56\u7565\uff1a\u6df7\u5408\u8bad\u7ec3\/\u6570\u636e\u6269\u589e\/\u5bf9\u6bd4\u8bad\u7ec3\u66f4\u52a0\u591a\u6837\u5316\u7684\u5e94\u7528\uff1a\u63a8\u8350\/\u836f\u7269\u5316\u5b66\/\u7269\u7406\u7cfb\u7edf\/NLP\/CV<\/p>\n

\u66f4\u52a0\u57fa\u7840\u7684\u7814\u7a76\uff1a\u8868\u793a\u80fd\u529b\/\u8fc7\u5e73\u6ed1\/\u4f20\u64ad\u673a\u5236\/\u707e\u96be\u6027\u9057\u5fd8<\/p>\n


\u968f\u7740GNN\u7814\u7a76\u7684\u6df1\u5165\uff0c\u4e00\u4e9b\u7814\u7a76\u8005\u4e0d\u5728\u4ec5\u4ec5\u5173\u6ce8\u4e8e\u8bbe\u8ba1\u6a21\u578b\u67b6\u6784\uff0c\u800c\u662f\u8bd5\u56fe\u6316\u6398\u548c\u89e3\u51b3GNN\u66f4\u52a0fundamental\u7684\u95ee\u9898\uff0c\u5982GNN\u7684\u8868\u793a\u80fd\u529b\u3002
\u5927\u90e8\u5206GNN\u7684\u8868\u793a\u80fd\u529b\u7684\u4e0a\u754c\u5c31\u662fWL-Test\uff0c\u5bf9\u4e8e\u540c\u6784\u7684\u56fe\u7ed3\u6784\u65e0\u6cd5\u533a\u5206\uff0c\u8fdb\u800c\u65e0\u6cd5\u5b66\u4e60\u5230\u6709\u533a\u5206\u5ea6\u7684\u8282\u70b9\u8868\u793a\u3002ID-GNN[1]\u901a\u8fc7\u4e00\u4e2a\u7b80\u5355\u7684ID\u589e\u5f3a\u7b56\u7565\u5c31\u53ef\u4ee5\u6781\u5927\u63d0\u5347GNN\u7684\u8868\u793a\u80fd\u529b\uff0c\u8ba9\u539f\u672c\u65e0\u6cd5\u533a\u5206\u7684\u56fe\u7ed3\u6784(\u6216\u8005GNN\u7684\u805a\u5408\u56fe)\u533a\u5206\u5f00\u6765\u3002
\u201c\u4f20\u64ad\u662fGNN\u7684\u672c\u8d28\u201d\u3002\u4f46\u662f\u6df1\u5c42GNN\u4f20\u64ad\u8fdc\u8ddd\u79bb\u7684\u4fe1\u606f\u4f1a\u5e26\u6765\u8fc7\u5e73\u6ed1\u73b0\u8c61\u5bfc\u81f4\u6a21\u578b\u6548\u679c\u4e0b\u964d\u3002GCC[2]\u7814\u7a76\u4e86GNN\u7684\u4f20\u64ad\u673a\u5236\uff0c\u5176\u4e0d\u4ec5\u89e3\u91ca\u4e86\u8fc7\u5e73\u6ed1\u7684\u672c\u8d28\uff0c\u8fd8\u89e3\u51b3\u4e86\u4e3a\u4ec0\u4e48GNN\u7684\u5404\u79cd\u53d8\u79cd\u53ef\u4ee5\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u7f13\u89e3\u8fc7\u5e73\u6ed1\u73b0\u8c61\u3002
\u707e\u96be\u6027\u9057\u5fd8\u6307\u6a21\u578b\u4f1a\u5fd8\u8bb0\u5148\u524d\u5b66\u4e60\u5230\u7684\u77e5\u8bc6\uff0c\u5728NN\u4e2d\u5df2\u7ecf\u6709\u4e86\u4e00\u4e9b\u7814\u7a76\u3002TWP[3]\u7814\u7a76\u4e86GNN\u4e0a\u7684\u9057\u5fd8\u95ee\u9898\u5e76\u63d0\u51fa\u4e86\u4e00\u79cd\u62d3\u6251\u611f\u77e5\u7684\u6743\u91cd\u4fdd\u7559\u6280\u672f\u6765\u514b\u670d\u4e0a\u8ff0\u95ee\u9898\u3002\u7c7b\u4f3c\u7684ER-GCN[4]\u5229\u7528\u7ecf\u9a8c\u56de\u653e\u673a\u5236\u6765\u5b9e\u73b0GNN\u5728\u8fde\u7eed\u4efb\u52a1\u4e0a\u7684\u6301\u7eed\u5b66\u4e60\uff0c\u4e5f\u53ef\u4ee5\u4e00\u5b9a\u7a0b\u5ea6\u7684\u53ef\u4ee5\u9057\u5fd8\u95ee\u9898\u3002<\/p>\n

\u66f4\u52a0\u590d\u6742\u7684\u56fe\u6570\u636e\uff1a\u5f02\u8d28\u56fe\/\u6709\u5411\u56fe\/\u52a8\u6001\u56fe<\/p>\n


\u5728GNN\u7684\u7814\u7a76\u521d\u671f\uff0c\u5927\u5bb6\u7684\u76ee\u5149\u4e3b\u8981\u96c6\u4e2d\u5728\u7b80\u5355\u540c\u8d28\u56fe(\u53ea\u6709\u4e00\u79cd\u8282\u70b9\u548c\u8fb9)\u4e0a\uff0c\u8fd9\u5927\u5e45\u5ea6\u964d\u4f4e\u4e86\u4ee3\u7801\u5b9e\u73b0\u7684\u96be\u5ea6\u3002\u4f8b\u5982\uff0c\u7ecf\u5178\u7684GCN\u53ea\u9700\u8981AXW\u5373\u53ef\u5b9e\u73b0\u3002\u4f46\u662f\uff0c\u5b9e\u9645\u60c5\u51b5\u5f80\u5f80\u66f4\u52a0\u590d\u6742\uff0c\u968f\u7740GNN\u7814\u7a76\u7684\u6df1\u5165\uff0c\u5927\u5bb6\u5f00\u59cb\u5173\u6ce8\u4e00\u4e9b\u66f4\u4e3a\u590d\u6742\u4e5f\u66f4\u6709\u5b9e\u9645\u4ef7\u503c\u7684\u56fe\u6570\u636e\uff0c\u5982\u5f02\u8d28\u56fe\u3001\u52a8\u6001\u56fe\u3001\u6709\u5411\u56fe\u548c\u8d85\u56fe\u7b49\u3002
\u8003\u8651\u5230\u591a\u79cd\u7c7b\u578b\u8282\u70b9\u4e4b\u95f4\u7684\u4e30\u5bcc\u4ea4\u4e92\uff0c\u4e3a\u4e86\u907f\u514d\u4fe1\u606f\u635f\u5931\uff0c\u6211\u4eec\u9700\u8981\u5c06\u5176\u5efa\u6a21\u4e3a\u5f02\u8d28\u56fe\u3002GraphMSE[5]\u5c31\u662f\u4e00\u79cd\u9488\u5bf9\u5f02\u8d28\u56fe\u6570\u636e\u8bbe\u8ba1\u7684GNN\uff0c\u5176\u5145\u5206\u6316\u6398\u4e86\u591a\u79cd\u4ea4\u4e92\u4e0b\u90bb\u5c45(\u7ed3\u6784)\u4fe1\u606f\u6765\u63d0\u5347\u8282\u70b9\u8868\u793a\u3002HGSL[6]\u5219\u63a2\u7d22\u4e86\u5f02\u8d28\u56fe\u7ed3\u6784\u5bf9\u4e8e\u8282\u70b9\u8868\u793a\u7684\u5f71\u54cd\uff0c\u901a\u8fc7\u5b66\u4e60\u66f4\u52a0\u7684\u51c6\u786e\u7684\u56fe\u7ed3\u6784\u6765\u63d0\u5347GNN\u7684\u8868\u73b0\u3002
\u5728\u5fae\u535a\u56fe\u4e0a\uff0c\u7528\u6237\u4e4b\u95f4\u6709\u5173\u6ce8\u6216\u8005\u62c9\u9ed1\u7b49\u5173\u7cfb\uff0c\u8fd9\u5b9e\u9645\u662f\u4e00\u79cd\u6709\u5411\u7b26\u53f7\u7f51\u7edc\u3002SDGNN[7]\u662f\u4e00\u79cd\u9488\u5bf9\u6709\u5411\u7b26\u53f7\u56fe\u8bbe\u8ba1\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\uff0c\u540c\u65f6\u8003\u8651\u4e86\u8fb9\u7684\u65b9\u5411\/\u7b26\u53f7(\u559c\u6b22\u4e3a\u6b63\uff0c\u8ba8\u538c\u4e3a\u8d1f)\u548c\u52a8\u6001\u56fe\u6f14\u5316\u7684\u76f8\u5173\u7406\u8bba(status theory \u548c balance theory)\u6765\u66f4\u597d\u7684\u5efa\u6a21\u52a8\u6001\u6027\u5e76\u5b9e\u73b0\u56fe\u7684\u8868\u793a\u5b66\u4e60\u3002
\u56fe\u6570\u636e\u5f80\u5f80\u662f\u52a8\u6001\u53d8\u5316\u7684\u3002HVGNN[8]\u5728\u53cc\u66f2\u7a7a\u95f4\u91cc\u5efa\u6a21\u4e86\u52a8\u6001\u56fe\u968f\u65f6\u95f4\u6f14\u5316\u7684\u7279\u6027\uff0c\u5176\u5f15\u5165\u4e86\u4e00\u79cd\u65f6\u95f4\u611f\u77e5\u7684\u6ce8\u610f\u529b\u673a\u5236(Tem- poral GNN)\u6765\u533a\u5206\u4e0d\u540c\u65f6\u95f4\u6bb5\u5185\u8282\u70b9\u7684\u5dee\u5f02\u3002RNN-GCN[9]\u5219\u662f\u5c06\u7ecf\u5178\u7684\u65f6\u5e8f\u6a21\u578bRNN\u5f15\u5165\u5230GNN\u4e2d\uff0c\u5229\u7528dynamic stochastic block\u6765\u6355\u83b7\u8282\u70b9\u548c\u793e\u533a\u7684\u6f14\u5316\u8fc7\u7a0b\uff0c\u8fdb\u800c\u5b9e\u73b0\u52a8\u6001\u56fe\u4e0a\u7684\u8282\u70b9\u805a\u7c7b\u3002<\/p>\n

\u66f4\u52a0\u4e30\u5bcc\u7684\u8bad\u7ec3\u7b56\u7565\uff1a\u6df7\u5408\u8bad\u7ec3\/\u6570\u636e\u6269\u589e\/\u5bf9\u6bd4\u8bad\u7ec3<\/p>\n


\u7ecf\u5178\u7684GNN(\u5305\u62ecGCN\u548cGAT)\u90fd\u662f\u4ee5\u534a\u76d1\u7763\u8282\u70b9\u5206\u7c7bLoss\u8fdb\u884c\u8bad\u7ec3\u7684\u3002\u968f\u540e\u7684\u7814\u7a76\u4e5f\u6cbf\u7740\u8fd9\u4e2a\u8def\u7ebf\uff0c\u5c06\u76ee\u5149\u96c6\u4e2d\u5728\u5982\u4f55\u8bbe\u8ba1\u66f4\u52a0\u7cbe\u5de7\u7684\u6a21\u578b\u67b6\u6784\u6765\u63d0\u5347\u6a21\u578b\u6548\u679c\u3002\u7684\u786e\uff0c\u590d\u6742\u7684\u6a21\u578b\u53ef\u4ee5\u63d0\u5347\u6548\u679c\uff0c\u4f46\u662f\u5176\u5f80\u5f80\u8d85\u53c2\u6570\u8f83\u591a\u4e14\u96be\u4ee5\u8bad\u7ec3\u3002\u76f8\u8f83\u4e8e\u8bbe\u8ba1\u65b0\u7684\u6a21\u578b\u67b6\u6784\uff0c\u4e00\u4e9b\u7814\u7a76\u8005\u5f00\u59cb\u63a2\u7d22\u5982\u4f55\u5229\u7528\u8bad\u7ec3\u7b56\u7565(\u5982\u6570\u636e\u6269\u589e)\u6765\u63d0\u5347\u73b0\u6709GNN\u6a21\u578b\u7684\u6548\u679c\u3002
GraphMix[10]\u6574\u5408\u4e86interpolation\u6570\u636e\u6269\u589e\u548cself-training\u6570\u636e\u6269\u589e\u6280\u672f\uff0c\u5c06\u7b80\u5355\u7684GCN\u67b6\u6784\u63d0\u5347\u5230\u63a5\u8fd1SOTA\u7684\u6548\u679c\u3002\u4f8b\u5982\uff0c\u539f\u59cb\u7684GCN\u5728Cora\u7684\u6548\u679c\u53ea\u670981.3\uff0c\u800cGraphMix\u8bad\u7ec3\u7b56\u7565\u53ef\u4ee5\u5c06GCN\u7684\u6548\u679c\u63d0\u5347\u81f383.94\u3002\u540c\u65f6\uff0cGraphMix\u65e0\u9700\u989d\u5916\u7684\u5185\u5b58\u6d88\u8017\uff0c\u8ba1\u7b97\u6d88\u8017\u4e5f\u51e0\u4e4e\u4e0d\u53d8\u3002
\u7c7b\u4f3c\u7684\uff0cGAUG[11]\u4e5f\u5c1d\u8bd5\u4ece\u6570\u636e\u6269\u589e\u7684\u89d2\u5ea6\u6765\u63d0\u5347\u73b0\u6709\u534a\u76d1\u7763GNN\u7684\u6548\u679c\u3002\u5177\u4f53\u6765\u8bf4\uff0cGAUG\u8bbe\u8ba1\u4e86\u4e00\u4e2aedge prediction\u6765\u7f16\u7801\u56fe\u4e0a\u8282\u70b9\u7684\u7c7b\u5185\u540c\u8d28\u7ed3\u6784\uff0c\u7136\u540e\u63d0\u5347\u7c7b\u5185\u8fb9\u7684\u6570\u91cf(\u79fb\u9664\u7c7b\u95f4\u8fb9)\u3002\u7136\u540e\uff0c\u57fa\u4e8e\u4fee\u6539\u540e\u7684\u66f4\u52a0\u7cbe\u51c6\u7684\u56fe\u7ed3\u6784\uff0c\u5728Cora\u6570\u636e\u96c6\u4e0a\uff0cGAUG\u5c06GCN\u7684\u6548\u679c\u63d0\u5347\u81f383.6\uff0c\u5c06GraphSAGE\u7684\u6548\u679c\u63d0\u5347\u81f383.2\u3002
\u4e0e\u4e0a\u8ff0\u4e24\u4e2a\u5de5\u4f5c\u4e0d\u540c\uff0cContrastive GCNs with Graph Generation (CG3)[12] \u5c1d\u8bd5\u5bf9\u6807\u7b7e\u8fdb\u884c\u589e\u5f3a\u3002\u5b9e\u9645\u4e0a\uff0c\u5982\u679c\u6ca1\u6709\u8db3\u591f\u7684\u76d1\u7763\u4fe1\u53f7\uff0c\u534a\u76d1\u7763\u5b66\u4e60Semi-Supervised Learning (SSL)\u7684\u6548\u679c\u90fd\u662f\u6709\u9650\u7684\u3002\u8003\u8651\u5230\u56fe\u4e0a\u534a\u76d1\u7763\u5b66\u4e60\u7684\u7279\u70b9\uff0c\u672c\u6587\u4e0d\u4ec5\u4ec5\u8003\u8651\u4e86\u540c\u7c7b\u6570\u636e\u4e0d\u540cview\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u8fd8\u5efa\u6a21\u4e86\u8282\u70b9\u5c5e\u6027\u548c\u56fe\u62d3\u6251\u7ed3\u6784\u4e4b\u95f4\u7684\u6f5c\u5728\u8054\u7cfb\u6765\u4f5c\u4e3a\u989d\u5916\u7684\u76d1\u7763\u4fe1\u53f7\u3002\u57fa\u4e8e\u589e\u5f3a\u540e\u7684\u56fe\u76d1\u7763\u4fe1\u53f7\uff0cCG3\u5728\u6807\u7b7e\u7387\u53ea\u67090.5%\u7684\u60c5\u51b5\u4e0b\uff0c\u53ef\u4ee5\u53d6\u5f978%\u5de6\u53f3\u7684\u7edd\u5bf9\u51c6\u786e\u7387\u63d0\u5347\uff01<\/p>\n

\u66f4\u52a0\u591a\u6837\u5316\u7684\u5e94\u7528\uff1a\u63a8\u8350\/\u836f\u7269\u5316\u5b66\/\u7269\u7406\u7cfb\u7edf\/NLP\/CV<\/p>\n


\u56fe\u4e0a\u7684\u94fe\u8def\u9884\u6d4b\u5b9e\u9645\u5c31\u662f\u63a8\u8350\u3002\u5c06GNN\u7528\u5230\u63a8\u8350\u4e2d\u662f\u975e\u5e38\u81ea\u7136\u7684\u4e00\u4ef6\u4e8b\u3002HGSRec[13]\u5c06\u5f02\u8d28\u56fe\u795e\u7ecf\u7f51\u7edc\u7528\u4e8e\u5efa\u6a21\u6dd8\u5b9d\u7528\u6237\u4e4b\u95f4\u7684\u5206\u4eab\u884c\u4e3a\uff0c\u9884\u6d4b\u4e86\u7528\u6237\u4e4b\u95f4\u7684\u4e09\u5143\u5206\u4eab\u884c\u4e3a\u3002GHCF[14]\u5c06\u63a8\u8350\u7cfb\u7edf\u4e2d\u591a\u6837\u7684\u7528\u6237-\u5546\u54c1\u4ea4\u4e92\u5efa\u6a21\u4e3a\u591a\u5173\u7cfb\u5f02\u8d28\u56fe\u5e76\u8bbe\u8ba1\u4e86\u76f8\u5e94\u7684\u56fe\u795e\u7ecf\u7f51\u7edc\u67b6\u6784\u6765\u5b9e\u73b0\u63a8\u8350\u3002DHCN[15]\u5efa\u6a21\u4e86Session-based Recommendation\u4e2d\u7684\u8d85\u56fe\u4ea4\u4e92\uff0c\u5229\u7528\u53cc\u901a\u9053\u7684\u8d85\u56fe\u5377\u79ef\u7f51\u7edc\u6765\u5b9e\u73b0\u5546\u54c1\u63a8\u8350\u3002<\/p>\n

\u56fe\u7ed3\u6784\u6570\u636e\u53ef\u4ee5\u5f88\u597d\u5730\u5efa\u6a21\u5206\u5b50\u53ca\u5176\u4e4b\u95f4\u7684\u5316\u5b66\u952e\u3002\u56e0\u6b64\uff0cAI\u5236\u836f\u5f00\u59cb\u5c1d\u8bd5\u5229\u7528GNN\u6765\u5b9e\u73b0\u836f\u7269\u5206\u5b50\u7684\u7814\u53d1(\u5982\u6027\u8d28\u9884\u6d4b\uff0c\u9006\u5408\u6210)\u3002GTA[16]\u5c06GNN\u7528\u4e8e\u836f\u7269\u5206\u5b50\u9886\u57df\u7684\u9006\u5408\u6210\u9884\u6d4b\u95ee\u9898\uff0cCAGG[17]\u5219\u662f\u4ece\u56fe\u751f\u6210\u7684\u89d2\u5ea6\u6765\u5b9e\u73b0\u836f\u7269\u5206\u5b50\u7684\u5408\u6210\u3002
MGTN[18]\u5c06\u56fe\u50cf\u6570\u636e\u8f6c\u4e3a\u56fe\u7ed3\u6784\u6570\u636e\uff0c\u5229\u7528\u56fe\u50cf\u4e2d\u4e0d\u540c\u76ee\u6807(\u5efa\u6a21\u4e3a\u5b50\u56fe)\u4e4b\u95f4\u7684\u5173\u7cfb\u5efa\u6a21\u6765\u5b9e\u73b0\u66f4\u597d\u7684\u591a\u7c7b\u56fe\u50cf\u5206\u7c7b\uff0c\u800cPC-RGNN[19]\u5c06\u70b9\u4e91\u6570\u636e\u5efa\u6a21\u4e3a\u56fe\uff0c\u5229\u7528\u56fe\u4e0a\u4e0d\u540c\u5c3a\u5ea6\u7684\u5173\u7cfb\u805a\u5408\u6765\u5f3a\u5316\u5176\u70b9\u4e91\u7684\u8868\u793a\u3002<\/p>\n

\u53c2\u8003\u6587\u732e\uff1aIdentity-aware Graph Neural Networks
Why Do Attributes Propagate in Graph Convolutional Neural Networks
Overcoming Catastrophic Forgetting in Graph Neural Networks
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
GraphMSE: Efficient Meta-path Selection in Semantically Aligned Feature Space for Graph Neural Networks
Heterogeneous Graph Structure Learning for Graph Neural Networks
SDGNN: Learning Node Representation for Signed Directed Networks
Hyperbolic Variational Graph Neural Network for Modeling Dynamic Graphs
Interpretable Clustering on Dynamic Graphs with Recurrent Graph Neural Networks
GraphMix: Improved Training of GNNs for Semi-Supervised Learning
Data Augmentation for Graph Neural Networks
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Who You Would Like to Share With? A Study of Share Recommendation in Social E-commerce
Graph Heterogeneous Multi-Relational Recommendation
Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation
GTA: Graph Truncated Attention for Retrosynthesis
Cost-Aware Graph Generation: A Deep Bayesian Optimization Approach
Modular Graph Transformer Networks for Multi-Label Image Classification
PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection<\/p>\n

\u590d\u6742\u79d1\u5b66\u6700\u65b0\u8bba\u6587


\u96c6\u667a\u6591\u56fe\u9876\u520a\u8bba\u6587\u901f\u9012\u680f\u76ee\u4e0a\u7ebf\u4ee5\u6765\uff0c\u6301\u7eed\u6536\u5f55\u6765\u81eaNature\u3001Science\u7b49\u9876\u520a\u7684\u6700\u65b0\u8bba\u6587\uff0c\u8ffd\u8e2a\u590d\u6742\u7cfb\u7edf\u3001\u7f51\u7edc\u79d1\u5b66\u3001\u8ba1\u7b97\u793e\u4f1a\u79d1\u5b66\u7b49\u9886\u57df\u7684\u524d\u6cbf\u8fdb\u5c55\u3002\u73b0\u5728\u6b63\u5f0f\u63a8\u51fa\u8ba2\u9605\u529f\u80fd\uff0c\u6bcf\u5468\u901a\u8fc7\u5fae\u4fe1\u670d\u52a1\u53f7\u300c\u96c6\u667a\u6591\u56fe\u300d\u63a8\u9001\u8bba\u6587\u4fe1\u606f\u3002\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\u5373\u53ef\u4e00\u952e\u8ba2\u9605\uff1a<\/p>\n

<\/div>\n

\u63a8\u8350\u9605\u8bfb<\/p>\n

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

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