{"id":2404,"date":"2025-01-15T21:06:14","date_gmt":"2025-01-15T13:06:14","guid":{"rendered":"https:\/\/www.15zhi.net\/blog\/?p=2404"},"modified":"2025-01-15T21:06:14","modified_gmt":"2025-01-15T13:06:14","slug":"202501-%e8%ae%ba%e6%96%87%e7%a0%94%e8%af%bb-revisiting-modularity-maximization-for-graph-clustering-a-contrastive-learning-perspective","status":"publish","type":"post","link":"https:\/\/www.15zhi.net\/blog\/202501-%e8%ae%ba%e6%96%87%e7%a0%94%e8%af%bb-revisiting-modularity-maximization-for-graph-clustering-a-contrastive-learning-perspective\/","title":{"rendered":"202501 \u8bba\u6587\u7814\u8bfb-Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective"},"content":{"rendered":"\n

\u6765\u6e90:KDD \u201924, August 25\u201329, 2024<\/p>\n\n\n\n

\u4f5c\u8005:Yunfei Liu, Jintang Li, Yuehe Chen\u2026.<\/p>\n\n\n\n

\u5355\u4f4d:\u8682\u8681\u96c6\u56e2<\/p>\n\n\n\n

\u4e00\u3001\u8bba\u6587\u4e3b\u8981\u5de5\u4f5c\u53ca\u8d21\u732e<\/h2>\n\n\n\n

\uff081\uff09\u5efa\u7acb\u4e86\u6a21\u5757\u5316\u6700\u5927\u5316\u548c\u56fe\u5bf9\u6bd4\u5b66\u4e60\u4e4b\u95f4\u7684\u8054\u7cfb\u3002 \u672c\u6587\u7684\u7814\u7a76\u7ed3\u679c\u8868\u660e\uff0c\u6a21\u5757\u5316\u6700\u5927\u5316\u53ef\u4ee5\u88ab\u89c6\u4e3a\u5229\u7528\u56fe\u4e2d\u7684\u6f5c\u5728\u793e\u533a\u4fe1\u606f\u8fdb\u884c\u5bf9\u6bd4\u5b66\u4e60\u3002<\/p>\n\n\n\n

\uff082\uff09Magi \u901a\u8fc7\u5229\u7528\u5e95\u5c42\u793e\u533a\u7ed3\u6784\u6765\u907f\u514d\u8bed\u4e49\u6f02\u79fb\uff0c\u5e76\u6d88\u9664\u4e86\u56fe\u589e\u5f3a\u7684\u9700\u8981\u3002 Magi \u91c7\u7528\u4e24\u9636\u6bb5\u968f\u673a\u6e38\u8d70\u65b9\u6cd5\uff0c\u4ee5\u5c0f\u6279\u91cf\u5f62\u5f0f\u6267\u884c\u6a21\u5757\u5316\u6700\u5927\u5316\u4efb\u52a1\uff0c\u4ece\u800c\u5b9e\u73b0\u826f\u597d\u7684\u53ef\u6269\u5c55\u6027\u3002<\/p>\n\n\n\n

\uff083\uff09\u4f5c\u8005\u5bf9 8 \u4e2a\u4e0d\u540c\u5c3a\u5ea6\u7684\u771f\u5b9e\u4e16\u754c\u56fe\u6570\u636e\u96c6\u8fdb\u884c\u4e86\u5e7f\u6cdb\u7684\u5b9e\u9a8c\u3002 Magi \u5728\u56fe\u805a\u7c7b\u4efb\u52a1\u4e2d\u59cb\u7ec8\u4f18\u4e8e\u591a\u9879\u6700\u5148\u8fdb\u7684\u6280\u672f\u3002 \u503c\u5f97\u6ce8\u610f\u7684\u662f\uff0cMagi \u53ef\u4ee5\u8f7b\u677e\u6269\u5c55\u5230\u5177\u6709 100M \u8282\u70b9\u7684\u5de5\u4e1a\u89c4\u6a21\u56fe\uff0c\u5c55\u793a\u4e86\u5176\u5728\u5927\u89c4\u6a21\u573a\u666f\u4e2d\u7684\u53ef\u6269\u5c55\u6027\u548c\u6709\u6548\u6027\u3002<\/p>\n\n\n\n

\u4e8c\u3001\u6a21\u578b\u6846\u67b6<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n

2.1 Modularity maximization pretext task<\/h2>\n\n\n\n

\u4e3a\u4e86\u9002\u5e94\u5c0f\u6279\u91cf\u8bad\u7ec3\u4ee5\u83b7\u5f97\u826f\u597d\u7684\u53ef\u6269\u5c55\u6027\uff0c\u4e00\u4e2a\u76f4\u89c2\u7684\u60f3\u6cd5\u662f\u968f\u673a\u91c7\u6837\u56fe\u4e2d\u7684\ud835\udc5b\u8282\u70b9\uff0c\u5e76\u4ece\u5176\u76f8\u5e94\u7684\u5b50\u6a21\u5757\u77e9\u9635\u4e2d\u83b7\u5f97\u5bf9\u6bd4\u5bf9\u3002\u4f46\u662f\u8fd9\u6837\u4f1a\u4ea7\u751f\u4e00\u4e9b\u95ee\u9898\uff1a
\u7ed3\u6784\u504f\u5dee\uff1a\u6240\u91c7\u6837\u7684\u7ed3\u6784\u4e0d\u80fd\u4ee3\u8868\u5b8c\u6574\u7684\u56fe\u7ed3\u6784\uff1b
\u7f3a\u4e4f\u9ad8\u9636\u90bb\u8fd1\u6027\uff1a\u4e24\u4e2a\u8282\u70b9\u901a\u5e38\u7531\u4e8e\u9ad8\u9636\u90bb\u8fd1\u6027\u800c\u5c5e\u4e8e\u540c\u4e00\u793e\u533a\uff0c\u4f8b\u5982\u5177\u6709\u8bb8\u591a\u5171\u540c\u7684\u90bb\u5c45\u3002<\/p>\n\n\n\n

\u4f5c\u8005\u63d0\u51fa\u4e86\u4e00\u79cd\u57fa\u4e8e\u4e24\u9636\u6bb5\u968f\u673a\u6e38\u8d70\u7684\u91c7\u6837\u65b9\u6cd5\u6765\u89e3\u51b3\u4e0a\u8ff0\u6311\u6218\uff0c\u901a\u8fc7\u8c03\u6574\u968f\u673a\u6e38\u8d70\u7684\u6df1\u5ea6\uff0c\u53ef\u4ee5\u6709\u6548\u5730\u6355\u83b7\u7f51\u7edc\u5185\u7684\u9ad8\u9636\u90bb\u8fd1\u5ea6\u3002
\u91c7\u7528\u7b2c\u4e00\u9636\u6bb5\u968f\u673a\u6e38\u8d70\u5bf9\u591a\u4e2a\u5b50\u793e\u533a\u8fdb\u884c\u91c7\u6837\uff0c\u5e76\u5c06\u5b83\u4eec\u5408\u5e76\u5230\u8bad\u7ec3\u6279\u6b21\u4e2d\uff0c\u786e\u4fdd\u8be5\u6279\u6b21\u5185\u7684\u76f8\u5e94\u5b50\u56fe\u5305\u542b\u6709\u6548\u793e\u533a\u3002
\u6267\u884c\u7b2c\u4e8c\u9636\u6bb5\u968f\u673a\u6e38\u8d70\u4ee5\u751f\u6210\u6279\u6b21\u5185\u8282\u70b9\u4e4b\u95f4\u7684\u76f8\u4f3c\u5ea6\u77e9\u9635\uff0c\u5e76\u5229\u7528\u914d\u7f6e\u6a21\u578b\u7684\u6982\u5ff5\u4ee5\u5c0f\u6279\u91cf\u5f62\u5f0f\u8ba1\u7b97\u76f8\u5e94\u7684\u6a21\u5757\u5ea6\u77e9\u9635\u3002<\/p>\n\n\n\n

2.2 Contrastive loss formulation<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u4e09\u3001\u5b9e\u9a8c\u7ed3\u679c<\/h2>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n

\u56db\u3001\u603b\u7ed3\u4e0e\u601d\u8003<\/h2>\n\n\n\n

\uff08\u4e00\uff09\u3001\u8bba\u6587\u7684\u6838\u5fc3\u5185\u5bb9:
1.\u901a\u8fc7\u795e\u7ecf\u6a21\u5757\u6027\u6700\u5927\u5316\u63a2\u7d22\u56fe\u805a\u7c7b\u95ee\u9898\u3002 \u5efa\u7acb\u4e86\u795e\u7ecf\u6a21\u5757\u6027\u6700\u5927\u5316\u548c\u56fe\u5bf9\u6bd4\u5b66\u4e60\u4e4b\u95f4\u7684\u8054\u7cfb\uff0c\u907f\u514d\u4e86\u6f5c\u5728\u7684\u8bed\u4e49\u6f02\u79fb\u548c\u53ef\u6269\u5c55\u6027\u95ee\u9898\u3002
2.\u4e3a\u4e86\u786e\u4fdd\u66f4\u597d\u7684\u53ef\u6269\u5c55\u6027\uff0cMagi \u91c7\u7528\u4e24\u9636\u6bb5\u968f\u673a\u6e38\u8d70\u4ee5\u5c0f\u6279\u91cf\u65b9\u5f0f\u903c\u8fd1\u6a21\u5757\u5316\u77e9\u9635\uff0c\u7136\u540e\u91c7\u7528\u539f\u5219\u6027\u5bf9\u6bd4\u635f\u5931\u6765\u4f18\u5316\u6a21\u5757\u5316\u6700\u5927\u5316\u7684\u76ee\u6807\uff08\u4e8c\uff09\u3001\u7efc\u5408\u5bf9\u9f50\u601d\u8003:<\/p>\n\n\n\n

\uff08\u4e8c\uff09\u3001\u7efc\u5408\u5bf9\u9f50\u601d\u8003:
1.\u6587\u7ae0\u4e2d\u7684\u5c0f\u6279\u91cf\u6a21\u5757\u5316\u77e9\u9635\u5728\u4e00\u5b9a\u7a0b\u5ea6\u4e0a\u53ef\u4ee5\u7f13\u89e3\u8d85\u5927\u6570\u636e\u96c6\u7684\u8d44\u6e90\u6d88\u8017\u95ee\u9898\uff0c\u4f46\u65e0\u6cd5\u4ece\u6839\u672c\u4e0a\u89e3\u51b3\u5c0f\u6279\u91cf\u4e2d\u7684\u7ed3\u6784\u504f\u5dee\u95ee\u9898\u3002
2.MAGI\u65b9\u6cd5\u53ef\u4ee5\u4f5c\u4e3a\u5bf9\u6bd4\u65b9\u6cd5\u6765\u8fdb\u884c\u6548\u679c\u6bd4\u5bf9\u3002<\/p>\n\n\n\n

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

<\/p>\n","protected":false},"excerpt":{"rendered":"

\u6765\u6e90:KDD \u201924, August 25\u201329, 2024 \u4f5c\u8005:Yunfei Liu, Jintang L […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2404","post","type-post","status-publish","format-standard","hentry","category-events"],"_links":{"self":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2404","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/comments?post=2404"}],"version-history":[{"count":1,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2404\/revisions"}],"predecessor-version":[{"id":2410,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/posts\/2404\/revisions\/2410"}],"wp:attachment":[{"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/media?parent=2404"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/categories?post=2404"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.15zhi.net\/blog\/wp-json\/wp\/v2\/tags?post=2404"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}