Deterministic Graph-Walking Program Mining
Peter Belcak, Roger Wattenhofer

TL;DR
This paper introduces a formal framework for mining deterministic graph-walking programs that identify linear long-distance relationships between vertex sets in complex graphs, with algorithms that generate programs of increasing length.
Contribution
It formalizes the concept of connection via graph-walking programs and provides algorithms for mining these programs to reveal relationships in graph data.
Findings
Algorithms successfully mine programs of increasing length
Programs characterize linear long-distance relationships
Framework formalizes connection in graphs
Abstract
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.
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Taxonomy
TopicsData Mining Algorithms and Applications · semigroups and automata theory · Logic, Reasoning, and Knowledge
