  In this work, we propose a Graph Convolutional Neural Networks (GCN) based
scheduling algorithm for adhoc networks. In particular, we consider a
generalized interference model called the $k$-tolerant conflict graph model and
design an efficient approximation for the well-known Max-Weight scheduling
algorithm. A notable feature of this work is that the proposed method do not
require labelled data set (NP-hard to compute) for training the neural network.
Instead, we design a loss function that utilises the existing greedy approaches
and trains a GCN that improves the performance of greedy approaches. Our
extensive numerical experiments illustrate that using our GCN approach, we can
significantly ($4$-$20$ percent) improve the performance of the conventional
greedy approach.
