Extracting node comparison insights for the interactive exploration of property graphs
Cristina Aguiar, Jacques Chabin, Alexandre Chanson, Mirian Halfeld-Ferrari, Nicolas Hiot, Nicolas Labroche, Patrick Marcel, Verónika Peralta, Felipe Vasconcelos
TL;DR
The paper addresses automatic extraction of node comparison insights in property graphs by introducing context-based indicators derived from node contexts and graph topology. It formalizes a problem of selecting indicators for grouping and comparing nodes, framed as a 3-partition optimization, and proposes several heuristics (Laplacian, local search, clustering, and random-start variants) to scale to real-world graphs. An end-to-end pipeline covers indicator collection and insight computation, leveraging percentile scaling and path-length contextualization, with Cypher-driven data extraction. Empirical evaluation on diverse real-world datasets demonstrates that simple heuristics rapidly yield actionable insights, while more sophisticated methods yield higher-quality results, enabling interactive exploratory data analysis on property graphs.
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.