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A Multi-Scale Process for Mining and Abstracting Environmental Spatio-Temporal Graphs

Authors Assaad Zeghina , Aurélie Leborgne , Florence Le Ber , Antoine Vacavant

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OASIcs.TIME.2026.7.pdf
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Subject Classification

ACM Subject Classification
  • Information systems → Data mining
  • Computing methodologies → Neural networks
  • Information systems → Geographic information systems
Keywords
  • Graph Deep Learning
  • Temporal Graphs
  • Pattern Mining
  • Temporal Analysis
  • Graph Abstraction
  • Subgraph Matching

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Abstract

Understanding the continuous evolution of environmental systems increasingly relies on tracking large-scale, heterogeneous data across shifting timelines, commonly structured as temporal multigraphs. While such representations are highly expressive, their temporal density and structural heterogeneity make the automated analysis of long-term transitions particularly challenging. In this paper, we propose a unified process for extracting and abstracting recurrent evolutionary patterns within time-varying multigraphs. The proposed pipeline articulates four successive stages: (1) a spatio-temporal modeling stage based on RCC-8 relations to capture both topological structures and the chronological evolution of geographic entities across consecutive time steps; (2) a frequent pattern mining stage designed to discover recurring temporal sequences within multigraph representations; (3) a pattern matching stage that enables efficient, multi-scale localization of these evolutionary signatures; and (4) a hierarchical abstraction stage relying on super-nodes to produce a compact, condensed representation of the graph’s historical progression. An interactive visual environment is additionally provided as a complementary exploration tool to inspect the timeline outputs of the pipeline. We report on the results obtained from historical graph analyses of CORINE Land Cover data across multiple European cities, demonstrating the process’s capacity to reveal recurrent environmental behaviors and meaningful, time-dependent trajectories of change.

Cite As Get BibTex

Assaad Zeghina, Aurélie Leborgne, Florence Le Ber, and Antoine Vacavant. A Multi-Scale Process for Mining and Abstracting Environmental Spatio-Temporal Graphs. In 33rd International Symposium on Temporal Representation and Reasoning (TIME 2026). Open Access Series in Informatics (OASIcs), Volume 146, pp. 7:1-7:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/OASIcs.TIME.2026.7

Author Details

Assaad Zeghina
  • Université de Strasbourg, CNRS, ENGEES, ICube UMR 7357, Strasbourg, France
Aurélie Leborgne
  • Université de Strasbourg, CNRS, ENGEES, ICube UMR 7357, Strasbourg, France
Florence Le Ber
  • Université de Strasbourg, CNRS, ENGEES, ICube UMR 7357, Strasbourg, France
Antoine Vacavant
  • Université Clermont Auvergne, CNRS, SIGMA Clermont, Institut Pascal, France

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