NetVec: A Scalable Hypergraph Embedding System
NetVec is introduced, a novel multi-level framework for scalable un-supervised hypergraph embedding that can be coupled with any graph embedding algorithm to produce embeddings of hypergraphs with millions of nodes and hyperedges in a few minutes.
Abstract
Many problems such as vertex classification and link prediction in network data can be solved using graph embeddings, and a number of algorithms are known for constructing such embed-dings. However, it is difficult to use graphs to capture non-binary relations such as communities of vertices. These kinds of complex relations are expressed more naturally as hypergraphs. While hypergraphs are a generalization of graphs, state-of-the-art graph embedding techniques are not adequate for solving prediction and classification tasks on large hypergraphs accurately in reasonable time. In this paper, we introduce NetVec, a novel multi-level framework for scalable un-supervised hypergraph embedding, that can be coupled with any graph embedding algorithm to produce embeddings of hypergraphs with millions of nodes and hyperedges in a few minutes.