GraphIA: Scalable and Robust Representation Learning on Graphs

Project Overview

Graph data appear in almost all disciplines. Developing machine learning algorithms for graphs is a crucial task, with a plethora of interdisciplinary applications. As a prominent paradigm, network representation learning aims to embed nodes in a low-dimensional space, preserving the structural properties of the network. Nevertheless, this is a challenging task for various reasons. In the era of data deluge, we need to handle massive graphs. Besides, the models should capture the inherent complex structure of the data involved. Furthermore, we often have to deal with unreliable data sources. To address such challenges, GraphIA aims to design expressive representation learning models capable of leveraging rich structural and information semantics towards improving learning performance, while also ensuring scalability and robustness. Two interdisciplinary applications will be addressed: detection of influential spreaders in complex networks and analysis of network data in bioinformatics.

Funding agency: ANR (French National Research Agency) under the JCJC framework
Duration: 01/02/2021 - 31/01/2025
Link to ANR abstract: ANR-20-CE23-0009-01 ANR logo



  • Fragkiskos D. Malliaros, Assistant Professor, Project Coordinator (Université Paris-Saclay, CentraleSupélec, Inria)
  • Laurent Duval, Research Scientist (IPF Energies Nouvelles)
  • Silviu Maniu, Assistant Professor (Université Paris-Saclay)
  • Hugues Talbot, Professor (Université Paris-Saclay, CentraleSupélec, Inria)
  • Alexandre Duval, PhD Student (Université Paris-Saclay, CentraleSupélec, Inria)
  • Surabhi Jagtap, PhD Student (Université Paris-Saclay, CentraleSupélec, Inria and IPF Energies Nouvelles)
  • Younes Belkouchi, PhD Student (Université Paris-Saclay, CentraleSupélec, Inria)


  • A. Duval and F.D. Malliaros. GraphSVX: Shapley Value Explanations for Graph Neural Networks. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD), Virtual, 2021.
    [Paper: PDF, arXiv (full version): PDF, Code: LINK]

  • S. Jagtap, A. Çelikkanat, A. Pirayre, F. Bidard, L. Duval, and F.D. Malliaros. Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings. In Proceeding of the European Signal Processing Conference (EUSIPCO), Virtual, 2021.
    [Paper: PDF, Code: LINK]

  • G. Panagopoulos and F.D. Malliaros. Influence Learning and Maximization. In Proceedings of the 21st International Conference on Web Engineering (ICWE), Online, 2021.
    [Paper: PDF, Tutorial Website: LINK]