  In many real-world network datasets such as co-authorship, co-citation, email
communication, etc., relationships are complex and go beyond pairwise.
Hypergraphs provide a flexible and natural modeling tool to model such complex
relationships. The obvious existence of such complex relationships in many
real-world networks naturaly motivates the problem of learning with
hypergraphs. A popular learning paradigm is hypergraph-based semi-supervised
learning (SSL) where the goal is to assign labels to initially unlabeled
vertices in a hypergraph. Motivated by the fact that a graph convolutional
network (GCN) has been effective for graph-based SSL, we propose HyperGCN, a
novel GCN for SSL on attributed hypergraphs. Additionally, we show how HyperGCN
can be used as a learning-based approach for combinatorial optimisation on
NP-hard hypergraph problems. We demonstrate HyperGCN's effectiveness through
detailed experimentation on real-world hypergraphs.
