the semi-supervised learning results. 2) JLGCN also outperforms all the other recent work on the original Citeseer and Cora datasets and our sub-sampled Pubmed dataset. These results demonstrate the effectiveness of JLGCN in conducting semi-supervised classification tasks on graph data.

[p[1.4in]{}p[0.4in]{}p[0.4in]{}p[0.4in]{}]{} **Algorithm** & **Citeseer** & **Cora** & **Pubmed\***\

  -----------------------
  Planetoid
  [@yang2016revisiting]
  -----------------------

  : Results of semi-supervised node classification. \[\*\] means we sub-sample 10,000 nodes to perform the experiments.[]{data-label="tab:results_citation"}

& 64.7 & 75.7 & 77.2\

  -----------------------------
  GCN
  [@Kipf2016SemiSupervisedCW]
  -----------------------------

  : Results of semi-supervised node classification. \[\*\] means we sub-sample 10,000 nodes to perform the experiments.[]{data-label="tab:results_citation"}

& 70.3 & 81.5 & 78.4\

  -------------------------
  GAT
  [@velivckovic2017graph]
  -------------------------

  : Results of semi-supervised node classification. \[\*\] means we sub-sample 10,000 nodes to perform the experiments.[]{data-label="tab:results_citation"}

& 72.5 & 83.0 & 78.8\

  ---------------
  GMNN
  [@qu2019gmnn]
  ---------------

  : Results of semi-supervised node classification. \[\*\] means we sub-sample 10,000 nodes to perform the experiments.[]{data-label="tab:results_citation"}

& 73.1 & 83.7 & 79.4\
JLGCN & **73.7** & **83.9** & **79.7**\

### Robustness test

We design two experiments to test the robustness of our model: 1) we lower the label ratio by employing less training data. We adopt five missing ratios {0, 0.25, 0.5, 0.75, 0.9} of training nodes to train and 