🤖 AI Summary
This work addresses the need for automated, expert-free profiling of property graph data by proposing the first end-to-end framework for automatic Graph Generation Dependency (GGD) discovery. Methodologically, it introduces an Answer Graph factorization representation to compress intermediate matching results, and integrates heuristic candidate generation with similarity-based verification for efficient, scalable approximate GGD mining. Its contributions are threefold: (1) it supports dual-granularity analysis—schema-level structural dependencies and pattern-attribute correlations; (2) it significantly reduces memory and runtime overhead compared to baseline approaches; and (3) it outputs a high-coverage, low-redundancy, semantically interpretable set of GGDs that precisely capture both topological constraints and attribute regularities in graphs. This framework establishes a foundational primitive for unsupervised graph data quality assessment and pattern understanding.
📝 Abstract
With the increasing use of graph-structured data, there is also increasing interest in investigating graph data dependencies and their applications, e.g., in graph data profiling. Graph Generating Dependencies (GGDs) are a class of dependencies for property graphs that can express the relation between different graph patterns and constraints based on their attribute similarities. Rich syntax and semantics of GGDs make them a good candidate for graph data profiling. Nonetheless, GGDs are difficult to define manually, especially when there are no data experts available. In this paper, we propose GGDMiner, a framework for discovering approximate GGDs from graph data automatically, with the intention of profiling graph data through GGDs for the user. GGDMiner has three main steps: (1) pre-processing, (2) candidate generation, and, (3) GGD extraction. To optimize memory consumption and execution time, GGDMiner uses a factorized representation of each discovered graph pattern, called Answer Graph. Our results show that the discovered set of GGDs can give an overview about the input graph, both schema level information and also correlations between the graph patterns and attributes.