Topological and domain Knowledge-based subgraph mining : application on protein 3D-structures
type: Dataset
description: This thesis is in the intersection of two proliferating research fields, namely data mining and bioinformatics. With the emergence of graph data in the last few years, many efforts have been devoted to mining frequent subgraphs from graph databases. Yet, the number of discovered frequentsubgraphs is usually exponential, mainly because of the combinatorial nature of graphs. Many frequent subgraphs are irrelevant because they are redundant or just useless for the user. Besides, their high number may hinder and even makes further explorations unfeasible. Redundancy in frequent subgraphs is mainly caused by structural and/or semantic similarities, since most discovered subgraphs differ slightly in structure and may infer similar or even identical meanings. In this thesis, we propose two approaches for selecting representative subgraphs among frequent ones in order to remove redundancy. Each of the proposed approaches addresses a specific type of redundancy. The first approach focuses on semantic redundancy where similarity between subgraphs is measured based on the similarity between their nodes' labels, using prior domain knowledge. The second approach focuses on structural redundancy where subgraphs are represented by a set of user-defined topological descriptors, and similarity between subgraphs is measured based on the distance between their corresponding topological descriptions. The main application data of this thesis are protein 3D-structures. This choice is based on biological and computational reasons. From a biological perspective, proteins play crucial roles in almost every biological process. They are responsible of a variety of physiological functions. From a computational perspective, we are interested in mining complex data. Proteins are a perfect example of such data as they are made of complex structures composed of interconnected amino acids which themselves are composed of interconnected atoms. Large amounts of protein structures are currently availa…
landing page: https://theses.hal.science/tel-00946989
identifier: NNT: 2013CLF22410
keyword: feature-selection
keyword: fouille-de-motifs
keyword: frequent-subgraph
keyword: graphe-representant-topologique
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-ohcomputer-science-csother-csoh
keyword: pattern-mining
keyword: protein-structure
keyword: representative-unsubstituted-subgraph
keyword: selection-de-motifs
keyword: sous-graphe-frequent
keyword: sous-graphe-representant-non-substitue
keyword: spiotherengineering-sciences-physicsother
keyword: structure-de-proteine
keyword: theses
keyword: topological-representative-subgraph
issued: 2026-05-06T09:28:01.943353
modified: 2026-05-06T09:28:01.943359
language: en
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

Topological and domain Knowledge-based subgraph mining : application on protein 3D-structures
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00946989
issued: 2026-05-06T09:28:01.959317
modified: 2026-05-06T09:28:01.916882