  Network representation learning and node classification in graphs got
significant attention due to the invent of different types graph neural
networks. Graph convolution network (GCN) is a popular semi-supervised
technique which aggregates attributes within the neighborhood of each node.
Conventional GCNs can be applied to simple graphs where each edge connects only
two nodes. But many modern days applications need to model high order
relationships in a graph. Hypergraphs are effective data types to handle such
complex relationships. In this paper, we propose a novel technique to apply
graph convolution on hypergraphs with variable hyperedge sizes. We use the
classical concept of line graph of a hypergraph for the first time in the
hypergraph learning literature. Then we propose to use graph convolution on the
line graph of a hypergraph. Experimental analysis on multiple real world
network datasets shows the merit of our approach compared to state-of-the-arts.
