Large Scale Hypergraph Computation

Abstract

Abstract As introduced in the previous chapters, the complexity of hypergraph computation is relatively high. In practical applications, the hypergraph may not be in a small scale, where we often encounter the scenario that the size of the hypergraph is very large. Therefore, hypergraph computation confronts the complexity issues in many applications. Therefore, how to handle large scale data is an important task. In this chapter, we discuss the computation methods for large scale hypergraphs and their applications. Two types of hypergraph computation methods are provided to handle large scale data, namely the factorization-based hypergraph reduction method and hierarchical hypergraph learning method. In the factorization-based hypergraph reduction method, the large scale hypergraph incidence matrix is reduced to two low-dimensional matrices. The computing procedures are conducted with the reduced matrices. This method can support the hypergraph computation with more than 10,000 vertices and hyperedges. On the other hand, the hierarchical hypergraph learning method splits all samples as some sub-hypergraphs and merges the results obtained from each sub-hypergraph computation. This method can support hypergraph computation with millions of vertices and hyperedges.