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AISTATS 2019: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning
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Confidence-based Graph Convolutional Networks for Semi-Supervised Learning

Source code for AISTATS 2019 paper: Confidence-based Graph Convolutional Networks for Semi-Supervised Learning.

Label prediction on node a by Kipf-GCN and ConfGCN (this paper). L0 is a’s true label. Shade intensity of a node reflects the estimated score of label L1 assigned to that node. Since Kipf-GCN is not capable of estimating influence of one node on another, it is misled by the dominant label L1 in node a’s neighborhood and thereby making the wrong assignment. ConfGCN, on the other hand, estimates confidences (shown by bars) over the label scores, and uses them to increase influence of nodes b and c to estimate the right label on a. Please refer to paper for more details.


  • Compatible with TensorFlow 1.x and Python 3.x.
  • Dependencies can be installed using requirements.txt.


  • We use citation network datasets: Cora, Citeseer, Pubmed, and CoraML for evaluation in our paper.
  • Cora, Citeseer, and Pubmed datasets was taken directly from here. CoraML dataset was taken from here and was placed in the same format as other datasets for semi-supervised settings.
  • contains all the datasets in the required format.

Evaluate pretrained model:

  • Run for setting up the environment and extracting the datasets and pre-trained models.
  • contains TensorFlow (1.x) based implementation of ConfGCN (proposed method).
  • Execute for evaluating pre-trained ConfGCN model on all four datasets.

Training from scratch:

  • Execute for setting up the environment and extracting datasets.

  • config/hyperparams.jsoncontains the best parameters for all four datasets.

  • For training ConfGCN run:

    python -data citeseer -name new_run


Please cite us if you use this code.

  author    = {Shikhar Vashishth and
               Prateek Yadav and
               Manik Bhandari and
               Partha Talukdar},
  title     = {Confidence-based Graph Convolutional Networks for Semi-Supervised
  journal   = {CoRR},
  volume    = {abs/1901.08255},
  year      = {2019},
  url       = {},
  archivePrefix = {arXiv},
  eprint    = {1901.08255},
  timestamp = {Sat, 02 Feb 2019 16:56:00 +0100},
  biburl    = {},
  bibsource = {dblp computer science bibliography,}

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