Repairing Property Graphs under PG-Constraints

2026-02-05

AI Summary

This work addresses the problem of consistency repair for property graphs under PG-Constraints involving recursion and negation. It proposes the first repairable subset supporting recursive constraints and designs a complete repair pipeline that enforces constraint satisfaction by modifying graph topology—specifically through deletions of nodes, edges, and, innovatively, labels. The introduction of label deletion significantly reduces repair cost, while automaton-based analysis is leveraged to characterize error structures. Three algorithms are implemented: Integer Linear Programming (ILP), a naive greedy approach, and an LP-guided greedy method. Experimental results demonstrate that incorporating label deletion reduces the total number of deletions by up to 59% compared to strategies that only remove nodes or edges. Furthermore, the LP-guided greedy algorithm achieves up to a 97% speedup over baseline methods while maintaining high repair quality.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Graph-based Machine Learning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Recent standardization efforts for graph databases lead to standard query languages like GQL and SQL/PGQ, and constraint languages like Property Graph Constraints (PG-Constraints). In this paper, we embark on the study of repairing property graphs under PG-Constraints. We identify a significant subset of PG-Constraints, encoding denial constraints and including recursion as a key feature, while still permitting automata-based structural analyses of errors. We present a comprehensive repair pipeline for these constraints to repair Property Graphs, involving changes in the graph topology and leading to node, edge and, optionally, label deletions. We investigate three algorithmic strategies for the repair procedure, based on Integer Linear Programming (ILP), a naive, and an LP-guided greedy algorithm. Our experiments on various real-world datasets reveal that repairing with label deletions can achieve a 59% reduction in deletions compared to node/edge deletions. Moreover, the LP-guided greedy algorithm offers a runtime advantage of up to 97% compared to the ILP strategy, while matching the same quality.

Problem

• Property Graphs
• PG-Constraints
• Graph Repair
• Denial Constraints
• Graph Databases

Innovation

• Property Graph Repair
• PG-Constraints
• Automata-based Error Analysis
• LP-guided Greedy Algorithm
• Label Deletion