arXiv subjects

Daan de Graaf

Publications and source records attributed to Daan de Graaf.

3 recordsLinked to original sources

arXiv2026

GraphAlg Playground: An Online Platform for Learning and Experimenting with the GraphAlg Language

The GraphAlg language for graph algorithms enables native support for user-defined graph analytics workloads in databases. In this demonstration, we present a web-based playground for writing and executing GraphAlg programs in the web browser, including an interactive tutorial explaining its key concepts. The playground runs inside the user's web browser without any installation, and is freely available under a permissive license as a reusable library. We present two demonstration scenarios of the publicly available playground website, showing how new users can learn to program in GraphAlg using the tutorial, while expert users can use the playground to prototype and validate their algorithms.

cs.DB↗

arXiv2026

Foundations of the GraphAlg Language

The GraphAlg domain-specific language for graph algorithms enables user-defined algorithms in graph databases. In this work we show how GraphAlg is built on top of the formal MATLANG language for matrix manipulation. Starting from MATLANG, we describe the extensions to MATLANG and the syntactic sugar needed to derive GraphAlg. Furthermore, we prove that any GraphAlg program can be simulated in an extension of for-MATLANG that supports simultaneous induction.

cs.DB↗

arXiv2026

Algorithm Support for Graph Databases, Done Right

Graph database query languages cannot express algorithms like PageRank, forcing costly data wrangling, while existing solutions such as algorithm libraries, vertex-centric APIs, and recursive CTEs lack the necessary combination of expressiveness, performance, and usability. We present GraphAlg: a domain-specific language for graph algorithms that compiles to relational algebra, enabling seamless integration with query processing pipelines. Built on linear algebra foundations, GraphAlg provides intuitive matrix operations that are amenable to aggressive optimization including sparsity analysis, loop-invariant code motion, and in-place aggregation. Our implementation in AvantGraph demonstrates significant code complexity reduction compared to SQL/Python and Pregel while achieving excellent performance on LDBC Graphalytics benchmarks. GraphAlg establishes that graph databases can serve as unified platforms for both queries and analytics.

cs.DB↗