Hypergraphs model complex, non-binary relationships such as co-authorships, social group memberships, and recommendation systems. Like traditional graphs, hypergraphs can grow large, posing challenges for storage, transmission, and query performance. We propose HyperCSA, a novel compression method for hypergraphs that maintains support for standard queries over the succinct representation. HyperCSA achieves compression ratios of 26 % to 79 % of the original file size on real-world hypergraphs-outperforming existing methods on all large hypergraphs in our experiments. HyperCSA also scales to larger datasets than existing approaches and evaluates neighbor queries 6 to 40 times faster on common real-world hypergraphs.

Constructing the Burrows-Wheeler transform (BWT) for long strings poses significant challenges regarding construction time and memory usage. We enable the use of multi-string BWT construction algorithms to construct the BWT of a long string by partitioning it into shorter substrings using a prefix of the suffix array. We prove that except for a single run of sentinel symbols $, the BWT of the shorter substrings is equal to the BWT of the long string. The proposed partitioning idea is applicable to string collections of any alphabet.

Neighborhood queries are the most common queries on graphs; thus, it is desirable to answer them efficiently on compressed data structures. Our full paper [1] presents a grammar-based compressor called Incidence-Type-RePair (ITR) for graphs with labeled nodes and labeled edges based on RePair. We applied ITR to network, version, and RDF graphs and we compared the performance of triple SPO queries on these compressed datasets generated by ITR and 4 state-of-the-art graph compression approaches. As shown in the figure below and in our full paper [1] , ITR outperforms the other graph compressors for all triple SPO queries except for the query-type ? P ?, while providing a compression size comparable to the other compressors. Thereby, ITR is 2 to 6 times faster than the fastest compared query-evaluation technique and up to 100 times faster than the slowest approach in our tests.