{"id":477375,"date":"2023-08-09T09:11:34","date_gmt":"2023-08-09T09:11:34","guid":{"rendered":""},"modified":"2023-09-05T11:14:34","modified_gmt":"2023-09-05T11:14:34","slug":"graph-neural-networks","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/vn\/wiki\/graph-neural-networks\/","title":{"rendered":"M\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh \u0111\u1ed3 th\u1ecb"},"content":{"rendered":"<h2>T\u1ed5ng quan v\u1ec1 m\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb<\/h2>\n<p>M\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb (GNN) th\u1ec3 hi\u1ec7n s\u1ef1 ph\u00e1t tri\u1ec3n \u0111\u00e1ng k\u1ec3 trong l\u0129nh v\u1ef1c h\u1ecdc m\u00e1y v\u00e0 tr\u00ed tu\u1ec7 nh\u00e2n t\u1ea1o, nh\u1eb1m thu th\u1eadp v\u00e0 thao t\u00e1c d\u1eef li\u1ec7u c\u00f3 c\u1ea5u tr\u00fac \u0111\u1ed3 th\u1ecb. V\u1ec1 c\u01a1 b\u1ea3n, GNN l\u00e0 m\u1ed9t lo\u1ea1i m\u1ea1ng th\u1ea7n kinh \u0111\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1eb7c bi\u1ec7t \u0111\u1ec3 ho\u1ea1t \u0111\u1ed9ng tr\u00ean d\u1eef li\u1ec7u c\u00f3 c\u1ea5u tr\u00fac d\u01b0\u1edbi d\u1ea1ng bi\u1ec3u \u0111\u1ed3, cho ph\u00e9p ch\u00fang gi\u1ea3i quy\u1ebft nhi\u1ec1u v\u1ea5n \u0111\u1ec1 kh\u00e1c nhau m\u00e0 m\u1ea1ng th\u1ea7n kinh truy\u1ec1n th\u1ed1ng g\u1eb7p kh\u00f3 kh\u0103n. \u0110i\u1ec1u n\u00e0y bao g\u1ed3m nh\u01b0ng kh\u00f4ng gi\u1edbi h\u1ea1n \u1edf vi\u1ec7c tr\u00ecnh b\u00e0y m\u1ea1ng x\u00e3 h\u1ed9i, h\u1ec7 th\u1ed1ng \u0111\u1ec1 xu\u1ea5t, gi\u1ea3i th\u00edch d\u1eef li\u1ec7u sinh h\u1ecdc v\u00e0 ph\u00e2n t\u00edch l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp m\u1ea1ng.<\/p>\n<h2>L\u1ecbch s\u1eed v\u00e0 s\u1ef1 xu\u1ea5t hi\u1ec7n c\u1ee7a m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh \u0111\u1ed3 th\u1ecb<\/h2>\n<p>Kh\u00e1i ni\u1ec7m GNN l\u1ea7n \u0111\u1ea7u ti\u00ean xu\u1ea5t hi\u1ec7n v\u00e0o \u0111\u1ea7u nh\u1eefng n\u0103m 2000 v\u1edbi c\u00f4ng tr\u00ecnh c\u1ee7a Franco Scarselli, Marco Gori v\u00e0 nh\u1eefng ng\u01b0\u1eddi kh\u00e1c. H\u1ecd \u0111\u00e3 ph\u00e1t tri\u1ec3n m\u00f4 h\u00ecnh M\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb ban \u0111\u1ea7u \u0111\u1ec3 ph\u00e2n t\u00edch v\u00f9ng l\u00e2n c\u1eadn c\u1ee5c b\u1ed9 c\u1ee7a m\u1ed9t n\u00fat theo ki\u1ec3u l\u1eb7p. Tuy nhi\u00ean, m\u00f4 h\u00ecnh ban \u0111\u1ea7u n\u00e0y ph\u1ea3i \u0111\u1ed1i m\u1eb7t v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c v\u1ec1 hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n v\u00e0 kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng.<\/p>\n<p>M\u00e3i cho \u0111\u1ebfn khi M\u1ea1ng th\u1ea7n kinh chuy\u1ec3n \u0111\u1ed5i (CNN) \u0111\u01b0\u1ee3c gi\u1edbi thi\u1ec7u tr\u00ean \u0111\u1ed3 th\u1ecb, th\u01b0\u1eddng \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 M\u1ea1ng chuy\u1ec3n \u0111\u1ed5i \u0111\u1ed3 th\u1ecb (GCN), GNN m\u1edbi b\u1eaft \u0111\u1ea7u \u0111\u01b0\u1ee3c ch\u00fa \u00fd nhi\u1ec1u h\u01a1n. C\u00f4ng tr\u00ecnh c\u1ee7a Thomas N. Kipf v\u00e0 Max Welling v\u00e0o n\u0103m 2016 \u0111\u00e3 ph\u1ed5 bi\u1ebfn r\u1ed9ng r\u00e3i kh\u00e1i ni\u1ec7m n\u00e0y, t\u1ea1o n\u1ec1n t\u1ea3ng v\u1eefng ch\u1eafc cho l\u0129nh v\u1ef1c GNN.<\/p>\n<h2>M\u1edf r\u1ed9ng ch\u1ee7 \u0111\u1ec1: M\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb<\/h2>\n<p>M\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb (GNN) t\u1eadn d\u1ee5ng c\u1ea5u tr\u00fac \u0111\u1ed3 th\u1ecb c\u1ee7a d\u1eef li\u1ec7u \u0111\u1ec3 \u0111\u01b0a ra d\u1ef1 \u0111o\u00e1n v\u1ec1 c\u00e1c n\u00fat, c\u1ea1nh ho\u1eb7c to\u00e0n b\u1ed9 \u0111\u1ed3 th\u1ecb. V\u1ec1 b\u1ea3n ch\u1ea5t, GNN coi c\u00e1c t\u00ednh n\u0103ng c\u1ee7a m\u1ed7i n\u00fat v\u00e0 c\u00e1c t\u00ednh n\u0103ng c\u1ee7a n\u00fat l\u00e2n c\u1eadn l\u00e0 \u0111\u1ea7u v\u00e0o \u0111\u1ec3 c\u1eadp nh\u1eadt t\u00ednh n\u0103ng c\u1ee7a n\u00fat th\u00f4ng qua vi\u1ec7c truy\u1ec1n v\u00e0 t\u1ed5ng h\u1ee3p tin nh\u1eafn. Qu\u00e1 tr\u00ecnh n\u00e0y th\u01b0\u1eddng \u0111\u01b0\u1ee3c l\u1eb7p l\u1ea1i trong nhi\u1ec1u l\u1ea7n l\u1eb7p, \u0111\u01b0\u1ee3c g\u1ecdi l\u00e0 \u201cl\u1edbp\u201d c\u1ee7a GNN, cho ph\u00e9p th\u00f4ng tin truy\u1ec1n qua m\u1ea1ng.<\/p>\n<h2>C\u1ea5u tr\u00fac b\u00ean trong c\u1ee7a m\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb<\/h2>\n<p>Ki\u1ebfn tr\u00fac GNN bao g\u1ed3m m\u1ed9t s\u1ed1 th\u00e0nh ph\u1ea7n c\u1ed1t l\u00f5i:<\/p>\n<ol>\n<li>C\u00e1c t\u00ednh n\u0103ng c\u1ee7a n\u00fat: M\u1ed7i n\u00fat trong bi\u1ec3u \u0111\u1ed3 ch\u1ee9a c\u00e1c t\u00ednh n\u0103ng ban \u0111\u1ea7u c\u00f3 th\u1ec3 d\u1ef1a tr\u00ean d\u1eef li\u1ec7u trong th\u1ebf gi\u1edbi th\u1ef1c ho\u1eb7c \u0111\u1ea7u v\u00e0o t\u00f9y \u00fd.<\/li>\n<li>C\u00e1c t\u00ednh n\u0103ng bi\u00ean: Nhi\u1ec1u GNN c\u0169ng s\u1eed d\u1ee5ng c\u00e1c t\u00ednh n\u0103ng t\u1eeb c\u00e1c bi\u00ean, th\u1ec3 hi\u1ec7n m\u1ed1i quan h\u1ec7 gi\u1eefa c\u00e1c n\u00fat.<\/li>\n<li>Truy\u1ec1n tin nh\u1eafn: C\u00e1c n\u00fat t\u1ed5ng h\u1ee3p th\u00f4ng tin t\u1eeb c\u00e1c n\u00fat l\u00e2n c\u1eadn \u0111\u1ec3 c\u1eadp nh\u1eadt c\u00e1c t\u00ednh n\u0103ng c\u1ee7a ch\u00fang, truy\u1ec1n \u201cth\u00f4ng \u0111i\u1ec7p\u201d m\u1ed9t c\u00e1ch hi\u1ec7u qu\u1ea3 tr\u00ean bi\u1ec3u \u0111\u1ed3.<\/li>\n<li>Ch\u1ee9c n\u0103ng \u0111\u1ecdc: Sau m\u1ed9t s\u1ed1 l\u1edbp truy\u1ec1n th\u00f4ng tin, ch\u1ee9c n\u0103ng \u0111\u1ecdc c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng \u0111\u1ec3 t\u1ea1o \u0111\u1ea7u ra \u1edf c\u1ea5p \u0111\u1ed9 bi\u1ec3u \u0111\u1ed3.<\/li>\n<\/ol>\n<h2>C\u00e1c t\u00ednh n\u0103ng ch\u00ednh c\u1ee7a M\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb<\/h2>\n<ul>\n<li><strong>Kh\u1ea3 n\u0103ng x\u1eed l\u00fd d\u1eef li\u1ec7u kh\u00f4ng th\u01b0\u1eddng xuy\u00ean:<\/strong> GNN v\u01b0\u1ee3t tr\u1ed9i trong vi\u1ec7c x\u1eed l\u00fd d\u1eef li\u1ec7u b\u1ea5t th\u01b0\u1eddng, trong \u0111\u00f3 m\u1ed1i quan h\u1ec7 gi\u1eefa c\u00e1c th\u1ef1c th\u1ec3 r\u1ea5t quan tr\u1ecdng v\u00e0 kh\u00f4ng d\u1ec5 d\u00e0ng b\u1ecb c\u00e1c m\u1ea1ng th\u1ea7n kinh truy\u1ec1n th\u1ed1ng n\u1eafm b\u1eaft.<\/li>\n<li><strong>T\u00ednh kh\u00e1i qu\u00e1t:<\/strong> GNN c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c \u00e1p d\u1ee5ng cho b\u1ea5t k\u1ef3 v\u1ea5n \u0111\u1ec1 n\u00e0o c\u00f3 th\u1ec3 \u0111\u01b0\u1ee3c bi\u1ec3u di\u1ec5n d\u01b0\u1edbi d\u1ea1ng bi\u1ec3u \u0111\u1ed3, khi\u1ebfn ch\u00fang tr\u1edf n\u00ean c\u1ef1c k\u1ef3 linh ho\u1ea1t.<\/li>\n<li><strong>B\u1ea5t bi\u1ebfn \u0111\u1ed1i v\u1edbi th\u1ee9 t\u1ef1 \u0111\u1ea7u v\u00e0o:<\/strong> GNN cung c\u1ea5p \u0111\u1ea7u ra b\u1ea5t bi\u1ebfn b\u1ea5t k\u1ec3 th\u1ee9 t\u1ef1 c\u1ee7a c\u00e1c n\u00fat trong bi\u1ec3u \u0111\u1ed3, \u0111\u1ea3m b\u1ea3o hi\u1ec7u su\u1ea5t nh\u1ea5t qu\u00e1n.<\/li>\n<li><strong>Kh\u1ea3 n\u0103ng n\u1eafm b\u1eaft c\u00e1c m\u1eabu c\u1ee5c b\u1ed9 v\u00e0 to\u00e0n c\u1ea7u:<\/strong> V\u1edbi ki\u1ebfn tr\u00fac \u0111\u1ed9c \u0111\u00e1o c\u1ee7a m\u00ecnh, GNN c\u00f3 th\u1ec3 tr\u00edch xu\u1ea5t c\u1ea3 m\u1eabu c\u1ee5c b\u1ed9 v\u00e0 to\u00e0n c\u1ea7u trong d\u1eef li\u1ec7u.<\/li>\n<\/ul>\n<h2>C\u00e1c lo\u1ea1i m\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb<\/h2>\n<table>\n<thead>\n<tr>\n<th>Lo\u1ea1i GNN<\/th>\n<th>S\u1ef1 mi\u00eau t\u1ea3<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>M\u1ea1ng t\u00edch ch\u1eadp \u0111\u1ed3 th\u1ecb (GCN)<\/td>\n<td>S\u1eed d\u1ee5ng ph\u00e9p to\u00e1n t\u00edch ch\u1eadp \u0111\u1ec3 t\u1ed5ng h\u1ee3p th\u00f4ng tin v\u00f9ng l\u00e2n c\u1eadn.<\/td>\n<\/tr>\n<tr>\n<td>M\u1ea1ng ch\u00fa \u00fd \u0111\u1ed3 th\u1ecb (GAT)<\/td>\n<td>\u00c1p d\u1ee5ng c\u01a1 ch\u1ebf ch\u00fa \u00fd \u0111\u1ec3 c\u00e2n nh\u1eafc \u1ea3nh h\u01b0\u1edfng c\u1ee7a c\u00e1c n\u00fat l\u00e2n c\u1eadn.<\/td>\n<\/tr>\n<tr>\n<td>M\u1ea1ng \u0111\u1eb3ng c\u1ea5u \u0111\u1ed3 th\u1ecb (GIN)<\/td>\n<td>\u0110\u01b0\u1ee3c thi\u1ebft k\u1ebf \u0111\u1ec3 n\u1eafm b\u1eaft c\u00e1c th\u00f4ng tin t\u00f4p\u00f4 kh\u00e1c nhau b\u1eb1ng c\u00e1ch ph\u00e2n bi\u1ec7t c\u00e1c c\u1ea5u tr\u00fac \u0111\u1ed3 th\u1ecb kh\u00e1c nhau.<\/td>\n<\/tr>\n<tr>\n<td>\u0110\u1ed3 th\u1ecbSAGE<\/td>\n<td>T\u00ecm hi\u1ec3u c\u00e1ch nh\u00fang n\u00fat quy n\u1ea1p, cho ph\u00e9p d\u1ef1 \u0111o\u00e1n d\u1eef li\u1ec7u kh\u00f4ng nh\u00ecn th\u1ea5y.<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>C\u00e1c \u1ee9ng d\u1ee5ng v\u00e0 th\u00e1ch th\u1ee9c c\u1ee7a M\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb<\/h2>\n<p>GNN c\u00f3 c\u00e1c \u1ee9ng d\u1ee5ng \u0111a d\u1ea1ng, t\u1eeb ph\u00e2n t\u00edch m\u1ea1ng x\u00e3 h\u1ed9i v\u00e0 tin sinh h\u1ecdc \u0111\u1ebfn d\u1ef1 \u0111o\u00e1n l\u01b0u l\u01b0\u1ee3ng truy c\u1eadp v\u00e0 x\u00e1c minh ch\u01b0\u01a1ng tr\u00ecnh. Tuy nhi\u00ean, h\u1ecd c\u0169ng ph\u1ea3i \u0111\u1ed1i m\u1eb7t v\u1edbi nh\u1eefng th\u00e1ch th\u1ee9c. V\u00ed d\u1ee5: GNN c\u00f3 th\u1ec3 g\u1eb7p kh\u00f3 kh\u0103n v\u1edbi kh\u1ea3 n\u0103ng m\u1edf r\u1ed9ng th\u00e0nh c\u00e1c bi\u1ec3u \u0111\u1ed3 l\u1edbn v\u00e0 vi\u1ec7c thi\u1ebft k\u1ebf bi\u1ec3u di\u1ec5n bi\u1ec3u \u0111\u1ed3 ph\u00f9 h\u1ee3p c\u00f3 th\u1ec3 ph\u1ee9c t\u1ea1p.<\/p>\n<p>Vi\u1ec7c gi\u1ea3i quy\u1ebft nh\u1eefng th\u00e1ch th\u1ee9c n\u00e0y th\u01b0\u1eddng li\u00ean quan \u0111\u1ebfn s\u1ef1 c\u00e2n b\u1eb1ng gi\u1eefa \u0111\u1ed9 ch\u00ednh x\u00e1c v\u00e0 hi\u1ec7u qu\u1ea3 t\u00ednh to\u00e1n, \u0111\u00f2i h\u1ecfi ph\u1ea3i thi\u1ebft k\u1ebf v\u00e0 th\u1eed nghi\u1ec7m c\u1ea9n th\u1eadn. C\u00e1c th\u01b0 vi\u1ec7n kh\u00e1c nhau nh\u01b0 PyTorch Geometric, DGL v\u00e0 Spektral c\u00f3 th\u1ec3 gi\u00fap qu\u00e1 tr\u00ecnh tri\u1ec3n khai v\u00e0 th\u1eed nghi\u1ec7m d\u1ec5 d\u00e0ng h\u01a1n.<\/p>\n<h2>So s\u00e1nh v\u1edbi c\u00e1c m\u1ea1ng th\u1ea7n kinh kh\u00e1c<\/h2>\n<table>\n<thead>\n<tr>\n<th>Di\u1ec7n m\u1ea1o<\/th>\n<th>GNN<\/th>\n<th>CNN<\/th>\n<th>RNN<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>C\u1ea5u tr\u00fac d\u1eef li\u1ec7u<\/td>\n<td>\u0110\u1ed3 th\u1ecb<\/td>\n<td>L\u01b0\u1edbi (v\u00ed d\u1ee5: h\u00ecnh \u1ea3nh)<\/td>\n<td>Tr\u00ecnh t\u1ef1 (v\u00ed d\u1ee5: v\u0103n b\u1ea3n)<\/td>\n<\/tr>\n<tr>\n<td>T\u00ednh n\u0103ng ch\u00ednh<\/td>\n<td>Khai th\u00e1c c\u1ea5u tr\u00fac \u0111\u1ed3 th\u1ecb<\/td>\n<td>Khai th\u00e1c kh\u00f4ng gian \u0111\u1ecba ph\u01b0\u01a1ng<\/td>\n<td>Khai th\u00e1c \u0111\u1ed9ng l\u1ef1c h\u1ecdc th\u1eddi gian<\/td>\n<\/tr>\n<tr>\n<td>C\u00e1c \u1ee9ng d\u1ee5ng<\/td>\n<td>Ph\u00e2n t\u00edch m\u1ea1ng x\u00e3 h\u1ed9i, ph\u00e2n t\u00edch c\u1ea5u tr\u00fac ph\u00e2n t\u1eed<\/td>\n<td>Nh\u1eadn d\u1ea1ng h\u00ecnh \u1ea3nh, ph\u00e2n t\u00edch video<\/td>\n<td>M\u00f4 h\u00ecnh h\u00f3a ng\u00f4n ng\u1eef, ph\u00e2n t\u00edch chu\u1ed7i th\u1eddi gian<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2>Quan \u0111i\u1ec3m v\u00e0 c\u00f4ng ngh\u1ec7 t\u01b0\u01a1ng lai cho m\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb<\/h2>\n<p>GNN \u0111\u1ea1i di\u1ec7n cho m\u1ed9t l\u0129nh v\u1ef1c \u0111ang ph\u00e1t tri\u1ec3n v\u1edbi ti\u1ec1m n\u0103ng to l\u1edbn \u0111\u1ec3 kh\u00e1m ph\u00e1 v\u00e0 c\u1ea3i ti\u1ebfn h\u01a1n n\u1eefa. Nh\u1eefng ph\u00e1t tri\u1ec3n trong t\u01b0\u01a1ng lai c\u00f3 th\u1ec3 bao g\u1ed3m x\u1eed l\u00fd bi\u1ec3u \u0111\u1ed3 \u0111\u1ed9ng, kh\u00e1m ph\u00e1 bi\u1ec3u \u0111\u1ed3 3D v\u00e0 ph\u00e1t tri\u1ec3n c\u00e1c ph\u01b0\u01a1ng ph\u00e1p \u0111\u00e0o t\u1ea1o hi\u1ec7u qu\u1ea3 h\u01a1n. S\u1ef1 k\u1ebft h\u1ee3p c\u1ee7a GNN v\u1edbi h\u1ecdc t\u0103ng c\u01b0\u1eddng v\u00e0 h\u1ecdc chuy\u1ec3n giao c\u0169ng mang l\u1ea1i nh\u1eefng h\u01b0\u1edbng nghi\u00ean c\u1ee9u \u0111\u1ea7y h\u1ee9a h\u1eb9n.<\/p>\n<h2>M\u1ea1ng th\u1ea7n kinh \u0111\u1ed3 th\u1ecb v\u00e0 m\u00e1y ch\u1ee7 proxy<\/h2>\n<p>Vi\u1ec7c s\u1eed d\u1ee5ng m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 gi\u00e1n ti\u1ebfp h\u1ed7 tr\u1ee3 ho\u1ea1t \u0111\u1ed9ng c\u1ee7a GNN. V\u00ed d\u1ee5: trong c\u00e1c \u1ee9ng d\u1ee5ng trong th\u1ebf gi\u1edbi th\u1ef1c li\u00ean quan \u0111\u1ebfn vi\u1ec7c thu th\u1eadp d\u1eef li\u1ec7u t\u1eeb nhi\u1ec1u ngu\u1ed3n tr\u1ef1c tuy\u1ebfn kh\u00e1c nhau (v\u00ed d\u1ee5: qu\u00e9t web \u0111\u1ec3 ph\u00e2n t\u00edch m\u1ea1ng x\u00e3 h\u1ed9i), m\u00e1y ch\u1ee7 proxy c\u00f3 th\u1ec3 h\u1ed7 tr\u1ee3 thu th\u1eadp d\u1eef li\u1ec7u \u1ea9n danh v\u00e0 hi\u1ec7u qu\u1ea3, c\u00f3 kh\u1ea3 n\u0103ng h\u1ed7 tr\u1ee3 x\u00e2y d\u1ef1ng v\u00e0 c\u1eadp nh\u1eadt b\u1ed9 d\u1eef li\u1ec7u bi\u1ec3u \u0111\u1ed3.<\/p>\n<h2>Li\u00ean k\u1ebft li\u00ean quan<\/h2>\n<ol>\n<li><a href=\"https:\/\/ieeexplore.ieee.org\/document\/9046288\" target=\"_new\" rel=\"noopener nofollow\">M\u1ed9t kh\u1ea3o s\u00e1t to\u00e0n di\u1ec7n v\u1ec1 m\u1ea1ng l\u01b0\u1edbi th\u1ea7n kinh \u0111\u1ed3 th\u1ecb<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1812.08434\" target=\"_new\" rel=\"noopener nofollow\">M\u1ea1ng n\u01a1-ron \u0111\u1ed3 th\u1ecb: \u0110\u00e1nh gi\u00e1 v\u1ec1 c\u00e1c ph\u01b0\u01a1ng ph\u00e1p v\u00e0 \u1ee9ng d\u1ee5ng<\/a><\/li>\n<li><a href=\"https:\/\/arxiv.org\/abs\/1812.04202\" target=\"_new\" rel=\"noopener nofollow\">H\u1ecdc s\u00e2u v\u1ec1 \u0111\u1ed3 th\u1ecb: M\u1ed9t cu\u1ed9c kh\u1ea3o s\u00e1t<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/rusty1s\/pytorch_geometric\" target=\"_new\" rel=\"noopener nofollow\">Th\u01b0 vi\u1ec7n h\u00ecnh h\u1ecdc PyTorch<\/a><\/li>\n<\/ol>","protected":false},"featured_media":468487,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477375","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about <mark>Graph Neural Networks: Harnessing Power from Graph-Structured Data<\/mark>","faq_items":[{"question":"What are Graph Neural Networks (GNNs)?","answer":"<p>Graph Neural Networks (GNNs) are a type of neural network designed to process and make predictions about data structured as a graph. They are particularly useful in problems where the relationships between entities are complex and cannot be efficiently captured by traditional neural networks.<\/p>"},{"question":"When was the concept of GNNs first introduced?","answer":"<p>The concept of Graph Neural Networks first emerged in the early 2000s with the work of Franco Scarselli, Marco Gori, and others. They laid the groundwork for future development of GNNs.<\/p>"},{"question":"How do GNNs work?","answer":"<p>GNNs operate by treating each node's features and its neighbors' features as inputs to update the node's feature through a process called message passing and aggregation. This process is often repeated for several iterations or \"layers\", which allows information to propagate through the network.<\/p>"},{"question":"What are some key features of GNNs?","answer":"<p>Key features of GNNs include their capability to handle irregular data, generalizability to any problem that can be represented as a graph, invariance to input order, and their ability to capture both local and global patterns in the data.<\/p>"},{"question":"What types of Graph Neural Networks exist?","answer":"<p>Several types of Graph Neural Networks exist, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), Graph Isomorphism Networks (GINs), and GraphSAGE.<\/p>"},{"question":"What are some applications of GNNs and what challenges do they face?","answer":"<p>Applications of GNNs are diverse and include social network analysis, bioinformatics, traffic prediction, and program verification. However, they do face challenges like scalability to large graphs and complexity in designing the appropriate graph representation.<\/p>"},{"question":"How do GNNs compare with other neural networks?","answer":"<p>Unlike Convolutional Neural Networks (CNNs) that exploit spatial locality in grid-like data (like images), and Recurrent Neural Networks (RNNs) that exploit temporal dynamics in sequential data (like text), GNNs exploit the graph structure in the data.<\/p>"},{"question":"What is the future of GNNs?","answer":"<p>The field of GNNs is rapidly growing, with potential for further exploration and improvement. Future developments may include handling dynamic graphs, exploring 3D graphs, and developing more efficient training methods.<\/p>"},{"question":"How can proxy servers be used with Graph Neural Networks?","answer":"<p>Proxy servers can indirectly support the operation of GNNs. In real-world applications like data collection from various online sources, proxy servers can assist in efficient and anonymous data collection, thereby aiding in the construction and updating of graph datasets.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477375","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/wiki\/477375\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media\/468487"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/vn\/wp-json\/wp\/v2\/media?parent=477375"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}