PG-HIVE: Hybrid Incremental Schema Discovery for Property Graphs

2025-11-30

AI Summary

The schema-less nature of property graphs severely hinders data integration, querying, and visualization, necessitating automatic discovery of node/edge types and inference of attribute types and constraints—without explicit schema annotations. To address this, we propose PG-HIVE, the first framework introducing an incremental hybrid schema discovery mechanism that jointly leverages locality-sensitive hashing (LSH) and attribute-label co-clustering. This enables fine-grained, scalable, and dynamic schema evolution while avoiding full recomputation. PG-HIVE operates in a fully unsupervised manner, achieving both high accuracy and efficiency. Extensive experiments demonstrate that PG-HIVE improves node-type and edge-type identification accuracy by 65% and 40%, respectively, and accelerates execution by up to 1.95× compared to state-of-the-art methods—establishing new performance benchmarks in schema-agnostic property graph analysis.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in 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
• Web Mining and Content Analysis: Topic discovery and tracking

Abstract

Property graphs have rapidly become the de facto standard for representing and managing complex, interconnected data, powering applications across domains from knowledge graphs to social networks. Despite the advantages, their schema-free nature poses major challenges for integration, exploration, visualization, and efficient querying. To bridge this gap, we present PG-HIVE, a novel framework for automatic schema discovery in property graphs. PG-HIVE goes beyond existing approaches by uncovering latent node and edge types, inferring property datatypes, constraints, and cardinalities, and doing so even in the absence of explicit labeling information. Leveraging a unique combination of Locality-Sensitive Hashing with property- and label-based clustering, PG-HIVE identifies structural similarities at scale. Moreover, it introduces incremental schema discovery, eliminating costly recomputation as new data arrives. Through extensive experimentation, we demonstrate that PG-HIVE consistently outperforms state-of-the-art solutions, in both accuracy (by up to 65% for nodes and 40% for edges), and efficiency (up to 1.95x faster execution), unlocking the full potential of schema-aware property graph management.

Problem

• Automatically discovers latent node and edge types in property graphs
• Infers property datatypes, constraints, and cardinalities without explicit labels
• Enables incremental schema discovery to avoid costly recomputation on new data

Innovation

• Hybrid incremental schema discovery for property graphs
• Uses Locality-Sensitive Hashing with property-label clustering
• Infers node types, edge types, datatypes, constraints, cardinalities