{"categories":["\u8ad6\u6587\u30e1\u30e2","GAN","\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af","GCN"],"html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fwww.nogawanogawa.com%2Fentry%2Fgraphgan\" title=\"\u3010\u8ad6\u6587\u30e1\u30e2\uff1aGraphGAN\u3011GraphGAN: Graph Representation Learning with Generative Adversarial Nets - Re:\u30bc\u30ed\u304b\u3089\u59cb\u3081\u308bML\u751f\u6d3b\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","published":"2018-12-24 21:51:35","blog_title":"Re:\u30bc\u30ed\u304b\u3089\u59cb\u3081\u308bML\u751f\u6d3b","title":"\u3010\u8ad6\u6587\u30e1\u30e2\uff1aGraphGAN\u3011GraphGAN: Graph Representation Learning with Generative Adversarial Nets","url":"https://www.nogawanogawa.com/entry/graphgan","version":"1.0","width":"100%","author_url":"https://blog.hatena.ne.jp/nogawanogawa/","type":"rich","blog_url":"https://www.nogawanogawa.com/","provider_name":"Hatena Blog","height":"190","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/n/nogawanogawa/20181224/20181224090725.jpg","author_name":"nogawanogawa","provider_url":"https://hatena.blog","description":"\u8ad6\u6587 \u8457\u8005 \u80cc\u666f \u76ee\u7684\u3068\u30a2\u30d7\u30ed\u30fc\u30c1 \u76ee\u7684 \u30a2\u30d7\u30ed\u30fc\u30c1 \u63d0\u6848\u624b\u6cd5 GraphGAN Framework Discriminator Optimization Generator Optimization Graph Softmax\u95a2\u6570 \u8a55\u4fa1 \u8a55\u4fa1\u74b0\u5883 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8 \u6bd4\u8f03\u5bfe\u8c61 \u305d\u306e\u4ed6 Empirical Study Link Prediction Node Classification Recommendation \u7d50\u8ad6 \u611f\u60f3 \u8ad6\u6587 [1711.08267] GraphGAN: Graph Representation Learning with Generative Adversarial Ne\u2026"}