Neural　networks　have　been　proved　efficient　in　improving　many　machine　learning　tasks　such　as　convolutional　neural　networks　and　recurrent　neural　networks　for　computer　vision　and　natural　language　processing,　respectively.　However,　the　inputs　of　these　deep　learning　paradigms　all　belong　to　the　type　of　Euclidean　structure,　e.g.,　images　or　texts.　It　is　difficult　to　directly　apply　these　neural　networks　to　graph-based　applications　such　as　node　classification　since　graph　is　a　typical　non-Euclidean　structure　in　machine　learning　domain.　Graph　neural　networks　are　designed　to　deal　with　the　particular　graph-based　input　and　have　received　great　developments　because　of　more　and　more　research　attention.　In　this　paper,　we　provide　a　comprehensive　review　about　applying　graph　neural　networks　to　the　node　classification　task.　First,　the　state-of-the-art　methods　are　discussed　and　divided　into　three　main　categories:　convolutional　mechanism,　attention　mechanism　and　autoencoder　mechanism.　Afterward,　extensive　comparative　experiments　are　conducted　on　several　benchmark　datasets,　including　citation　networks　and　co-author　networks,　to　compare　the　performance　of　different　methods　with　diverse　evaluation　metrics.　Finally,　several　suggestions　are　provided　for　future　research　based　on　the　experimental　results.