This　study　proposes　a　novel　Graph　Convolutional　Neural　Network　with　Data-driven　Graph　Filter　(GCNN-DDGF)　model　that　can　learn　hidden　heterogeneous　pairwise　correlations　between　stations　to　predict　station-level　hourly　demand　in　a　large-scale　bike-sharing　network.　Two　architectures　of　the　GCNN-DDGF　model　are　explored;　GCNN(reg)-DDGF　is　a　regular　GCNN-DDGF　model　which　contains　the　convolution　and　feedforward　blocks,　and　GCNN(rec)-DDGF　additionally　contains　a　recurrent　block　from　the　Long　Short-term　Memory　neural　network　architecture　to　capture　temporal　dependencies　in　the　bike-sharing　demand　series.　Furthermore,　four　types　of　GCNN　models　are　proposed　whose　adjacency　matrices　are　based　on　various　bike-sharing　system　data,　including　Spatial　Distance　matrix　(SD),　Demand　matrix　(DE),　Average　Trip　Duration　matrix　(ATD),　and　Demand　Correlation　matrix　(DC).　These　six　types　of　GCNN　models　and　seven　other　benchmark　models　are　built　and　compared　on　a　Citi　Bike　dataset　from　New　York　City　which　includes　272　stations　and　over　28　million　transactions　from　2013　to　2016.　Results　show　that　the　GCNN(rec)-DDGF　performs　the　best　in　terms　of　the　Root　Mean　Square　Error,　the　Mean　Absolute　Error　and　the　coefficient　of　determination　(R-2),　followed　by　the　GCNN(reg)-DDGF.　They　outperform　the　other　models.　Through　a　more　detailed　graph　network　analysis　based　on　the　learned　DDGF,　insights　are　obtained　on　the　＂black　box＂　of　the　GCNN-DDGF　model.　It　is　found　to　capture　some　information　similar　to　details　embedded　in　the　SD,　DE　and　DC　matrices.　More　importantly,　it　also　uncovers　hidden　heterogeneous　pairwise　correlations　between　stations　that　are　not　revealed　by　any　of　those　matrices.