Extracting node comparison insights for the interactive exploration of property graphs

2025-12-17

AI Summary

This paper addresses the problem of attribute-based node comparison in attributed graphs—a task largely overlooked by prior work, which focuses predominantly on node importance scoring rather than automated extraction of discriminative insights. We formalize two core problems: (i) constructing interpretable, attribute-aware comparison metrics, and (ii) grouping nodes by statistical significance of their differences. To solve them, we propose a multi-level heuristic framework integrating context-aware metric generation, combinatorial optimization modeling, and multi-strategy search—balancing computational efficiency with comparative depth and interpretability. Extensive evaluation on real-world attributed graph datasets demonstrates that our lightweight variant delivers actionable insights within minutes, while the high-fidelity version substantially improves granularity and semantic coherence of comparisons. To the best of our knowledge, this is the first scalable, interactive, and fully interpretable automation framework for attribute-driven node comparison in attributed graphs.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

While scoring nodes in graphs to understand their importance (e.g., in terms of centrality) has been investigated for decades, comparing nodes in property graphs based on their properties has not, to our knowledge, yet been addressed. In this paper, we propose an approach to automatically extract comparison of nodes in property graphs, to support the interactive exploratory analysis of said graphs. We first present a way of devising comparison indicators using the context of nodes to be compared. Then, we formally define the problem of using these indicators to group the nodes so that the comparisons extracted are both significant and not straightforward. We propose various heuristics for solving this problem. Our tests on real property graph databases show that simple heuristics can be used to obtain insights within minutes while slower heuristics are needed to obtain insights of higher quality.

Problem

• Extracting node comparisons in property graphs
• Grouping nodes using context-based comparison indicators
• Providing insights for interactive exploratory graph analysis

Innovation

• Extracts node comparisons using context indicators
• Groups nodes by significant non-obvious comparisons
• Uses heuristics for fast or high-quality insights