Convolutional　neural　networks　have　been　shown　successful　in　extracting　features　from　images　and　texts.　However,　it　is　difficult　to　apply　convolutional　neural　networks　directly　on　ubiquitous　graph　data　since　the　graph　data　lies　in　an　irregular　structure.　A　significant　number　of　researchers　engrossed　themselves　in　studying　graph　convolutional　networks　transformed　from　Euclidean　domain.　Previous　graph　convolutional　networks　overviews　mainly　focus　on　reviewing　recent　methods　in　a　comprehensive　ways.　In　this　survey,　we　review　the　convolutional　networks　from　the　perspective　of　receptive　fields.　Roughly,　the　convolutional　networks　fall　into　three　main　categories:　spectral　based　methods,　sampling　based　methods　and　attention　based　methods.　We　analysis　the　differences　of　these　methods　and　propose　three　potential　directions　for　future　research　of　graph　convolutional　networks.