Scalable Hypergraph Embedding System
This addresses the need for scalable hypergraph embedding to solve prediction and classification tasks in network data, representing an incremental improvement over existing methods.
The paper tackles the problem of generating embeddings for large hypergraphs efficiently, introducing HyperNetVec, a hierarchical framework that produces high-quality embeddings for hypergraphs with millions of nodes and hyperedges in minutes, compared to days or failure with existing systems.
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.