2019-ICLR-Confidence-based Graph Convolutional Networks for Semi

Confidence-based Graph Convolutional Networks for Semi-Supervised

Learning

Shikhar Vashishth, Prateek Yadav, Manik Bhandari, Partha Talukdar

28 Sep 2018 (modified: 11 Oct 2018) ICLR 2019 Conference Withdrawn Submission Readers: Everyone 3 Replies

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� Abstract: Predicting properties of nodes in a graph is an important problem with applications in a

variety of domains. Graph-based Semi Supervised Learning (SSL) methods aim to address this problem

by labeling a small subset of the nodes as seeds, and then utilizing the graph structure to predict label

scores for the rest of the nodes in the graph. Recently, Graph Convolutional Networks (GCNs) have

experiments on standard benchmarks, we find that ConfGCN is able to significantly outperform state-of-

the-art baselines. We have made ConfGCN’s source code available to encourage reproducible research.

� Keywords: Graph Convolutional Networks, GCN, Confidence, Semi-Supervised Learning, Deep

Learning, Neural Networks

� TL;DR: We propose a confidence based Graph Convolutional Network for Semi-Supervised Learning.

Shikhar Vashishth

Indian Institute of Science, Bangalore

Names

Shikhar Vashishth (Preferred)

Emails

Indian Institute of Science, Bangalore (iisc.ac.in)

2016 – Present

Undergrad student

BITS Pilani, Pilani campus (pilani.bits-pilani.ac.in)

2012 – 2016

Advisors, Relations & Conflicts