Scalable Hypergraph Embedding System

TL;DR

This paper presents HyperNetVec, a scalable and efficient hypergraph embedding system that can handle large hypergraphs with millions of nodes and hyperedges in minutes, outperforming existing methods in speed and scalability.

Contribution

Introduces HyperNetVec, a hierarchical framework leveraging shared-memory parallelism for fast, high-quality hypergraph embeddings on large-scale data.

Findings

HyperNetVec generates embeddings for large hypergraphs in minutes.

Existing hypergraph systems are slower or fail on large datasets.

HyperNetVec achieves high-quality embeddings efficiently.

Abstract

Many problems such as node classification and link prediction in network data can be solved using graph embeddings. However, it is difficult to use graphs to capture non-binary relations such as communities of nodes. 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 HyperNetVec, a novel hierarchical framework for scalable unsupervised hypergraph embedding. HyperNetVec exploits shared-memory parallelism and is capable of generating high quality embeddings for real-world hypergraphs with millions of nodes and hyperedges in only a couple of minutes while existing hypergraph systems either fail for such large hypergraphs or may take days to produce the embeddings.