Learning to Localize Anomalies and Optimize Itineraries Via An Ai Framework for Combinatorial Optimization in Temporal Graphs
il y a 2 jours
**Learning to localize anomalies and optimize itineraries via an AI framework for combinatorial optimization in temporal graphs**:
- Réf **ABG-131021**
- Sujet de Thèse
- 14/04/2025
- Autre financement public
- LISIC EA 4491, Université du Littoral Côte d'Opale
- Lieu de travail- Saint-Omer - Les Hauts de France - France
- Intitulé du sujet- Learning to localize anomalies and optimize itineraries via an AI framework for combinatorial optimization in temporal graphs
- Champs scientifiques- Informatique
- Science de la donnée (stockage, sécurité, mesure, analyse)
- Mots clés- Artificial intelligence, temporal graphs, combinatorial optimisation, data mining, neural networks, deep learning
**Description du sujet**:
This PhD project explores a new direction for tackling these problems using artificial intelligence. While heuristic methods exist, they often struggle to balance speed and accuracy when coping with temporal graphs. In contrast, recent advances show that AI models can be trained to solve combinatorial problems on static graphs efficiently, yet their potential remains largely unexplored in the temporal graph setting. This project aims to bridge that gap by developing AI-based methods that learn to solve combinatorial optimization problems directly on temporal graphs.
This PhD project aims to explore the potential of machine learning methods as a means to efficiently solve combinatorial optimization problems on temporal graphs. We target three specific goals:
Goal 1: End-to-end learning framework.
We aim to design a framework that trains neural models to directly map problem instances to solutions in temporal graphs. While such approaches exist for static graphs, our challenge is to extend them to the temporal setting by defining suitable loss functions and training strategies.
Goal 2: A novel filter-based architecture.
We plan to develop a neural architecture that treats optimization as a filtering task — discarding irrelevant links to isolate the optimal subgraph. Building on spectral methods and recent work in temporal graph signal processing, we will explore how filters can be effectively defined and learned in a frequency-structure domain.
Anomaly localization: Many systems detect anomalies but fail to pinpoint their origin. We aim to learn to localize anomalies without relying on assumptions about their structure.
Temporal graph exploration: In transportation networks, finding optimal exploration routes is NP-hard. Our goal is to develop practical AI-based methods that scale better than current approximations.
**Nature du financement**:
- Autre financement public
**Précisions sur le financement**:
- ANR Project ANR-23-CPJ1-0048-01
**Présentation établissement et labo d'accueil**:
- LISIC EA 4491, Université du Littoral Côte d'Opale
**Site web**:
**Intitulé du doctorat**:
- Doctorat en Informatique
**Pays d'obtention du doctorat**:
- France
**Etablissement délivrant le doctorat**:
- Université du Littoral Côte d'Opale
**Ecole doctorale**:
- ECOLE DOCTORALE EN SCIENCES, TECHNOLOGIE ET SANTE
Starting date: October 2025
Duration: 3 years
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