  Influence maximization in complex networks, i.e., maximizing the size of
influenced nodes via selecting K seed nodes for a given spreading process, has
attracted great attention in recent years. However, the influence maximization
problem in hypergraphs, in which the hyperedges are leveraged to represent the
interactions among more than two nodes, is still an open question. In this
paper, we propose an adaptive degree-based heuristic algorithm, i.e., Heuristic
Degree Discount (HDD), which iteratively selects nodes with low influence
overlap as seeds, to solve the influence maximization problem in hypergraphs.
We further extend algorithms from ordinary networks as baselines and compare
the performance of the proposed algorithm and baselines on both real data and
synthetic hypergraphs. Results show that HDD outperforms the baselines in terms
of both effectiveness and efficiency. Moreover, the experiments on synthetic
hypergraphs indicate that HDD shows high performance, especially in hypergraphs
with heterogeneous degree distribution.
