Offre en lien avec l’Action/le Réseau : – — –/Doctorants
Laboratoire/Entreprise : IMT Atlantique LabSTICC CNRS (Brest); AIRBUS DS (É
Durée : 3 years
Contact : cecile.bothorel@imt-atlantique.fr
Date limite de publication : 2025-03-20
Contexte :
Summary: This thesis focuses on anomaly detection, explanation, and labeling in complex networks. We would like to explore and to propose a new adaptive and hybrid explanation method that incorporates interactions with domain experts using preference models from the MCDA field.
Location: IMT Atlantique LabSTICC CNRS (Brest); AIRBUS DS (Élancourt)
Keywords: Complex Networks, Graphs Autoencoders, Anomaly Detection, Explainable AI (XAI), Multi-Criteria Decision Aiding (MCDA)
Supervisors : Cécile Bothorel, Lina Fahed, Arwa Khannoussi, Guillaume Gadek
Funding: COFUND SEED (Co-funded by the European Union) https://www.imt-atlantique.fr/en/research-innovation/phd/seed
Eligibility rules: (1) Did not spend more than 12 months in France since 20 March 2022 (last 36 months). (2) Is / will be awarded a master-level diploma or equivalent for Phd start (from September 2025) (he/she can be graduated during summer) and does not already have a doctoral degree.
To apply: https://seed-apply.imt-atlantique.fr
Application deadline: March 20, 2025
Detailed subject: https://www.imt-atlantique.fr/sites/default/files/recherche/doctorat/seed/research-topics/4-anomaly-detection.html
Starting date: fall 2025
For any question: please contact supervisors cecile.bothorel@imt-atlantique.fr & guillaume.gadek@airbus.com
Sujet :
1. Definition
Keywords: Complex Networks, Graphs Autoencoders, Anomaly Detection, Explainable AI (XAI), Multi-Criteria Decision Aiding (MCDA)
1.1. Domain and scientific/technical context
Temporal graphs, representing interactions over time, are crucial for analyzing datasets in areas like Industry 4.0, finance, transportation, biology, social networks, cybersecurity, and defence and intelligence. Detecting anomalies in temporal networks reveals unusual patterns and events, thus providing deep insights into the system behavior over time. Such graphs or networks typically grow every second and gather millions of attributed nodes and edges. Relevant behaviors are grounded in the nodes and edges characteristics as well as in higher-level patterns (local neighborhood, temporal similarities). Operational needs are based on constant monitoring (anomaly detection, alerts), for which there is a very strong need for tools: for the detection in itself, but also for the understanding of the detected anomalies in order to enable quick and relevant responses and preventive measures. Notions of traceability and actionability of the alert are also key to the adoption of the technology.
1.2. Scientific/technical challenges
This thesis focuses on anomaly detection, understanding, and labeling in complex networks for socially impactful applications such as social networks, financial exchange, health, defence, energy, etc. The two main challenges are: (i) the limited access to labeled data for anomaly detection, (ii) and when labels are obtained, they are often incorrect or unusable due to errors made by domain experts in labeling anomalies. To address these challenges, we propose to take advantage of three research areas: anomaly detection (for graphs), explainable AI (XAI), multi-criteria decision aiding (MCDA).
In order to detect anomalies, we will study the GNNs (Graph Neural Networks), and the use of auto-encoders de-signed for semi-supervised tasks with a small training set even if it contains labeling errors [1]. Several graph explanations methods have been proposed in the literature [2] that focus on different graph elements (nodes, edges, features). Both graph elements describing anomalies and explanations are criteria that experts can use to label anomalies. However, this may not provide actionable insights as experts may focus on intuitions derived from previous expertise. The challenge here is to provide experts with intuitive graph elements and explanations allowing to understand the anomalies.
1.3. Considered methods, targeted results and impacts
We propose a new adaptive and hybrid explanation method that incorporates interactions with experts. This can be done using preference models from the MCDA field, which allow the representation of decision strategies and human behaviour [3]. We expect to:
Provide explanations generated from traditional XAI methods [8] and a combination of dedicated eval-uation metrics.
Enrich and adapt explanations with multiple criteria related to multiple domain experts. Such criteria include the experts’ decision strategies, their behaviours, and insights into their prior expertise.
Iteratively involve experts in the loop, i.e., the interaction between the explanation method and the experts can be performed iteratively in such a way that at the end the experts are given the intuitive graph elements and explanations they need to understand the anomalies well and to label them correctly.
In this project, we plan to develop an experimental protocol on both synthetic and real-world impact datasets. This work will be an important step forward in the field of anomaly detection and understanding, and will open important perspectives related to the intersection of our different research domains.
1.4. Environment (partners, places, specific tools and hardware)
The academic partners are members of the DECIDE team at Lab-STICC (CNRS) and IMT Atlantique’s Data Science Department (DSD) in Brest, where interdisciplinary research exploit synergies between decision support and data science to address scientific, industrial and societal issues arising from decision-making problems in complex sys- tems (environment, transport, energy, social networks, health, defence).
The industrial partner, Airbus Defence and Space, is participating through its team of Artificial Intelligence for De- fence Digital. The team, based in Elancourt near Paris, is constituted of 20 data scientists, and contributes on re- search, technology development and deployment of AI assets within Airbus products, mainly in the Defence & In- telligence areas.
Airbus provides 3 use cases with datasets and interaction with business experts related to the use cases, all dir- ectly related to the Intelligence business. A) detecting coordinated behaviour in social networks for Cyber Inform- ation Warfare. B) highlighting patterns and edges of interest in communication interceptions (COMINT), most likely through simulated data. C) Smart assistant for investigation analyst on Knowledge Graphs: the product Massive Intelligence extracts & generates high-level data under the form of entities and relations, through the IKDB software. The tool would help the end-user to raise alerts on the extracted knowledge itself, highlighting suspicious cases and connections.
1.5. Interdisciplinarity aspects
The work combines 3 research domains: anomaly detection (for graphs), explainable AI (XAI), and multi-criteria decision aiding (MCDA). This thesis involves both theoretical, experimental and technical research to to serve the industrial interests and applications of Airbus Defence&Space.
1.6. References
[1] GILES, Bastien, JEUDY, Baptiste, LARGERON, Christine, et al. Suspicious: a Resilient Semi-Supervised Framework for Graph Fraud Detection. IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), 2023.
[2] YUAN, Hao, YU, Haiyang, GUI, Shurui, et al. Explainability in graph neural networks: A taxonomic survey. IEEE transactions on pattern analysis and machine intelligence, 2022.
[3] KHANNOUSSI, Arwa, OLTEANU, Alexandru-Liviu, MEYER, Patrick, et al. A metaheuristic for inferring a ranking model based on multiple reference profiles. Annals of Mathematics and Artificial Intelligence, 2024.
[4] GADEK, Guillaume. “From community detection to topical, interactive group detection in Online Social Networks.” IEEE/WIC/ACM International Conference on Web Intelligence-Companion Volume. 2019.
[5] PRIEUR, Maxime, et al. “Shadowfax: Harnessing textual knowledge base population.” Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2024.
[6] BAUTISTA, Esteban, BRISSON, Laurent, BOTHOREL, Cécile, SMITS, Grégory. “MAD: Multi-Scale Anomaly Detection in Link Streams”. The 17th ACM International Conference on Web Search and Data Mining, Mar 2024, Mérida (Yucatan), Mexico.
[7] DAO, Vinh-Loc, BOTHOREL, Cécile, LENCA, Philippe. Community structure: A comparative evaluation of community detection methods. Network Science, 2020, 8 (1), pp.1-41.
[8] CHRAIBI-KAADOUD, Ikram , FAHED, Lina, LENCA, Philippe. Explainable AI: a narrative review at the crossroad of Knowledge Discovery, Knowledge Representation and Representation Learning. MRC@IJCAI 2021: Twelfth International Workshop Modelling and Reasoning in Context, 2021, pp.28-40.
2. Partners and study periods
2.1. Supervisors and study periods
IMT Atlantique: Prof.Cécile Bothorel, Assoc.-Prof. Lina Fahed and Assoc.-Prof. Arwa Khannoussi, IMT Atlantique, Brest, France.
Industrial partner: Dr. Guillaume Gadek, Airbus Defence and Space, Versailles, France
The PhD student will stay 9 months at Airbus Defence and Space.
Academic international partner(s): The PhD student will also spent 3 months at an international academic partner, probably LUT University, Finland (to be confirmed).
2.2. Hosting organizations
2.2.1. IMT Atlantique
IMT Atlantique, internationally recognized for the quality of its research, is a leading French technological university under the supervision of the Ministry of Industry and Digital Technology. IMT Atlantique maintains privileged relationships with major national and international industrial partners, as well as with a dense network of SMEs, start-ups, and innovation networks. With 290 permanent staff, 2,200 students, including 300 doctoral students, IMT Atlantique produces 1,000 publications each year and raises 18€ million in research funds.
2.2.2. Airbus Space and Defence
Airbus Space and Defence purpose is to improve life on Earth and beyond through our cutting-edge space technologies. From in-orbit delivery of satellites and spacecraft equipment to the smallest electronic components, Airbus provides products and services to customers around the world. We deliver telecommunications and navigation satellites that enable people to connect everywhere and navigate safely on Earth. The data from Airbus-built Earth observation satellites, such as Sentinel-2 or MetOp, bring insight that helps us to better understand and protect our planet.
Profil du candidat :
Formation et compétences requises :
Adresse d’emploi :
IMT Atlantique, campus Brest
Document attaché : 202502251404_4-anomaly-detection.pdf