# Deterministic Graph-Walking Program Mining

arXiv: https://arxiv.org/abs/2208.10290 · HTML: https://mlsanity.com/paper/2208.10290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 4 of 4)

- [Program Synthesis using Natural Language](https://mlsanity.com/paper/1509.00413/) (2015)
- [Program Synthesis using Conflict-Driven Learning](https://mlsanity.com/paper/1711.08029/) (2017)
- [Component-based Synthesis of Table Consolidation and Transformation Tasks from Examples](https://mlsanity.com/paper/1611.07502/) (2016)
- [Neural Program Synthesis with Query](https://mlsanity.com/paper/2205.07857/) (2022)

## Abstract (arXiv)

Owing to their versatility, graph structures admit representations of intricate relationships between the separate entities comprising the data. We formalise the notion of connection between two vertex sets in terms of edge and vertex features by introducing graph-walking programs. We give two algorithms for mining of deterministic graph-walking programs that yield programs in the order of increasing length. These programs characterise linear long-distance relationships between the given two vertex sets in the context of the whole graph.