Temporal graph patterns by timed automata
Amir Pouya Aghasadeghi, Jan Van den Bussche, Julia Stoyanovich

TL;DR
This paper introduces a novel approach using timed automata to express and evaluate complex temporal graph patterns, enabling more flexible and efficient detection of motifs in evolving graphs.
Contribution
It proposes a general framework for temporal graph pattern matching with timed automata and develops algorithms for efficient retrieval and evaluation of matches.
Findings
On-demand incremental algorithm excels with cyclic patterns on sparse graphs.
Maintaining partial matchings improves performance on acyclic or dense graphs.
Experimental results reveal trade-offs between different evaluation strategies.
Abstract
Temporal graphs represent graph evolution over time, and have been receiving considerable research attention. Work on expressing temporal graph patterns or discovering temporal motifs typically assumes relatively simple temporal constraints, such as journeys or, more generally, existential constraints, possibly with finite delays. In this paper we propose to use timed automata to express temporal constraints, leading to a general and powerful notion of temporal basic graph pattern (BGP). The new difficulty is the evaluation of the temporal constraint on a large set of matchings. An important benefit of timed automata is that they support an iterative state assignment, which can be useful for early detection of matches and pruning of non-matches. We introduce algorithms to retrieve all instances of a temporal BGP match in a graph, and present results of an extensive experimental…
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Taxonomy
TopicsDNA and Biological Computing · Genomics and Phylogenetic Studies · Caching and Content Delivery
