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Treffer: Multi-SPMiner: A Deep Learning Framework for Multi-Graph Frequent Pattern Mining with Application to spatiotemporal Graphs

Title:
Multi-SPMiner: A Deep Learning Framework for Multi-Graph Frequent Pattern Mining with Application to spatiotemporal Graphs
Authors:
Zeghina, Assaad Oussama, Leborgne, Aurelie, Le Ber, Florence, Vacavant, Antoine
Contributors:
Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie (ICube), École Nationale du Génie de l'Eau et de l'Environnement de Strasbourg (ENGEES)-Université de Strasbourg (UNISTRA)-Hôpitaux Universitaires de Strasbourg (HUS)-Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Matériaux et Nanosciences Grand-Est (MNGE), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Chimie - CNRS Chimie (INC-CNRS)-Centre National de la Recherche Scientifique (CNRS)-Réseau nanophotonique et optique, Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS)
Source:
27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems (KES 2023). :1094-1103
Publisher Information:
CCSD; Elsevier, 2023.
Publication Year:
2023
Collection:
collection:INSERM
collection:CNRS
collection:ENGEES
collection:UNIV-STRASBG
collection:INSA-STRASBOURG
collection:INC-CNRS
collection:AGREENIUM
collection:SITE-ALSACE
collection:INSA-GROUPE
collection:UNIVOAK
collection:UMR7357
Subject Terms:
Multigraph, Graph Neural Network, Frequent Motif Mining, Spatiotemporal Graphs, Graph Mining, [INFO.INFO-LG]Computer Science [cs], Machine Learning [cs.LG]
Subject Geographic:
Athènes, Greece
Original Identifier:
HAL: hal-04589913
Document Type:
Konferenz conferenceObject<br />Conference papers
Language:
English
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.procs.2023.10.097
DOI:
10.1016/j.procs.2023.10.097
Access URL:
https://hal.science/hal-04589913
https://hal.science/hal-04589913v1/document
https://hal.science/hal-04589913v1/file/islandora_171031.pdf
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.hal.04589913v1
Database:
HAL

Weitere Informationen

Mining frequent patterns in multigraphs is a challenging task in graph analysis with numerous real-world applications. This paper introduces a novel framework for frequent pattern mining on multi-graphs using the multi-SPMiner method. The approach is inspired by SPMiner, which was the first approach to employ deep learning in graph motif mining tasks. Multi-SPMiner builds on this foundation and focuses on the extraction of frequent motifs in single multi-graphs, specifically spatiotemporal graphs. Multi-SPMiner employs a two-step approach to extract the most frequent motifs in a graph with a high support value. In the first step, it embeds the nodes into an embedding order space, and in the second step, it performs a walk in the space to obtain the frequent motifs by iteratively growing the motif starting from a single node. The results obtained highlight the effectiveness of the proposed approach in identifying frequent motifs in single multigraphs, which is a crucial task in many real-world applications. Moreover, we demonstrate that our method is a generalization of SPMiner by testing it on single connection graphs.