Large Scale Hypergraph Computation

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