Graphs are widely used in many research fields to model very different pĥenomenons. This ubiquity which correspond to a strength of the graph representation is also a weakness since it tends to dissolve the research community working on graphs in many sub research fields with their own definitions, notations and sub research topics. The numerous specialized conferences further tends to close these communities around their thematic hence ignoring some advances performed in other communities and possible fruitful collaborations.
The aim of this summer school consists in presenting during a single week different approaches related to data analysis by/with graphs. Namely : Graphs and algorithmic, semantic graphs, dynamic graphs, signal processing on graphs, and machine learning on graphs.
One full day will be devoted to each thematic with more basic lectures in the morning so that people from different research communities may be able to follow the whole school and learn from the other communities. We hope that this school will provide fruitful discussions and collaborations.
The event will take place from Monday June 24, 2024 to Friday June 28, 2024 at INSA Rouen Normandie
Take care to the fact that some talks are scheduled in French. However, if too many nonFrench speakers are registered for a talk we will ask to the speaker if he can swich to English.
Inscriptions/Registration
Registration are now closed
IAPR grants are available for non European citizens. These persons will be contacted by the organizers after their pre registration.
Information on transportation and accomodations are available here

Monday June 24, 2024: Graphs and algorithmic
Hourly  Speaker  Title & abstract 

10h12h00  Paul Dorbec  When almost all problems are hard…
where we recall the definition of a few graph problems, manipulate them, and discover the complexity reductions and gadgets that have been put in place. The aim is to show, however, that hard instances are often quite specific A very large number of common graph problems are NPhard. In order to demonstrate this difficulty, we generally use gadgets, which are specific patterns in the graph that model another problem known to be NPhard, typically 3SAT. The instances produced in this way are highly constrained. The aim of this first talk is to introduce the definitions that will be used in the rest of the day and to highlight the specificity of the graphs that appear in complexity proofs. We will also show to what extent a small relaxation of the problem or a small variation of the graph makes the problem easy on the instances. The talk will be accessible and highly interactive, and will be given jointly with Aline Parreau. 
14h15h30  Aline Parreau  . . .but at the same time often easy
where we will see efficient algorithms, approximation, dynamic programming using graph properties… In this presentation, following on from the previous one, we will show more generally how certain properties of graphs can be exploited to find more efficient algorithms. Indeed, as soon as we assume that the graph has a certain structure (e.g. a bounded tree width or topological constraints), most problems are no longer NPhard or can be approximated efficiently. We will illustrate this on several examples using approximation algorithms or dynamic programming to find fast algorithms. We will also discuss the notion of kernel and FPT schemes if time permits. The talk will be accessible and highly interactive, and will be given jointly with Paul Dorbec. 
Lunch time  
15h4517h30  Arnaud De Mesmay  Planar or almost planar graphs: topology to the rescue of algorithms
Many graphs encountered in practice have a particular structure. For example, road networks have few or no intersections when drawn in a plane. We will see how this type of property, whether topological or geometric, interacts with the combinatorics of graphs, and often leads to the development of algorithms that are more efficient than in the general case. 
Tuesday June 25 : Semantic graphs

Time Speaker Title & Abstract 9h10h15 Géraldine Del Mondo Introduction to graph modelling lecture/tutorials The aim of this session is to present the modelling of real phenomena using graphs. An introduction to the general principles associated with this type of modelling will highlight the semantic aspects that need to be preserved in order to best express reality in the model. The difficulty lies in the compromise between a model that is too complex for automatic processing, but accurate and close to the initial phenomenon, and a model that is too highlevel and does not capture enough detail to address the underlying issues. In this context, approaches that allow the model to be viewed at different levels of abstraction could potentially provide an answer to this question.
A second section will present a few examples of graphbased models, which will show that, depending on the phenomena being modelled, graphs can have very different structures and characteristics (e.g. simple graph/hypergraph, static/dynamic, spatialised). Finally, in the third section, participants will be invited to reflect collectively on an example of a phenomenon and to ’produce’ a corresponding graph model.
10h3012h Aurélie Leborgne Genericity of semantic graph models, application to medical and environmental domains — conference This presentation focuses on the use of semantic graph models, in particular spatiotemporal graph models, in two different domains: functional MRI (medical domain) and land use (environmental do main). We will also discuss the use of this type of model for data mining and, more specifically, the search for frequent patterns. Semantic graphs are a method of representing data that enables relationships between entities to be visualized. Spatiotemporal graph models are an extension of this method, allowing spatial and temporal relationships between entities to be represented. This enables finer, more complete modelling of data in areas where position and time are key factors.
Two examples of the use of spatiotemporal graph models will be presented. The first example will be in the medical field with functional MRI, where spatiotemporal graphs are used to represent functional connections between different brain regions over time. The second example will be in the environmental domain with land use, where spatiotemporal graphs are used to represent the spatial and temporal relationships between different types of land use over time.
By modeling large quantities of data, it is possible to extract useful information. More specifically, the search for frequent patterns makes it possible to extract subgraphs that appear frequently in a spatiotemporal graph, which can shed light on a phenomenon that is repeated over time, or a set of entities with a spatial link that evolve over time.
Finally, based on data mining, we will be able to discuss the use of models in machine learning.
Lunch Time 14h15h30 Christophe Claramunt Graphbased Modelling Opportunities for Exploring Human Mobilities in the Era of the Cyberspace – conférence
The advent of information and communication technology, the Internet of Things, intelligent sensors, pervasive networks, and social media have led our society toward a digital and cyberspace era that offers novel avenues for the understanding of human activities that happen in space and time. Cyberspace is an open, global, unregulated, and virtual area of decentralized human activities, social interactions, and application services whose data, whether voluntarily or involuntarily created at unprecedented rates of dissemination, originate from a variety of user communities, ranging from experts to the general public, and different supports from social media to mobile users, but are not always well structured because they are most often not generated for further manipulation. This course introduces recent new ideas and graphbased modelling frameworks that reflect the spatial and temporal structures that emerge from cyberspace. The talk will introduce a series of theoretical, conceptual, methodological, and graphbased computational advances and contributions of this digital revolution to the study of mobility patterns in urban, regional and maritime environments. This short course will give a panorama of recent advances and will be illustrated by a series of research projects, and will discuss a few perspectives for further work.
Keywords: Graphbased modelling, Big data, social media, sensors, GIS, mobility, transportation studies
16h3018h Social Touristic tour of Rouen with a dinner in a restaurant.
Wednesday 26th June : Dynamic graphs
Horaire  Intervenant  Intitulé & Résumé  

8h3010h  Mathilde Vernet  Introduction to dynamic graphs. Models, paths, connectivity.
We know how to define a graphs, study them and study the associated problems, but what happens when we add a temporal dimension to a graph ? If it is observed over time and allowed to evolve over time, how is this new graph defined ? What happens to the basic definitions and notions of graph theory in this new context ? And what about the wellknown optimization problems in graphs ? Our aim is to provide (at least partial) answers to these questions. 

10h10h30  break  
10h3012h  David Ilcinkas  Distributed algorithms and dynamic graphs
We will look at the links between distributed algorithms and dynamic graphs. In particular, we will see how classical distributed methods and reasoning can be used to find elegant solutions to even centralised dynamic graph problems. 

12h0014h  Lunch time  
14h0015h30  Frédéric Guinand  Tutorial : Distributed algorithms in dynamic graphs  
15h3017h00  Yoann Pigné  Practical : detecting and monitoring communities in dynamic graphs 

Thursday 27th June: Signal Processing on Graphs
Horaire  Intervenant  Intitulé & Résumé 

8h4510h15  Pierre Borgnat  An invitation to signal processing on graphs, or Joseph Fourier as data scientist
With the explosion in the number and diversity of digital data, ana lysing data on graphs has become an inescapable challenge when it comes to studying interacting entities, in addition to any characte ristics of their own. Among the various approaches to this, we will present the advances made over the last twelve years in graph signal processing, which offers tools for processing and analysing data on graphs and graphs themselves. At the heart of this work, the Fourier transform equivalents of data (or signals) on graphs have made it possible to develop new tools for filtering, interpolation, denoising or multiscale decomposition of signals on graphs, but also offer impor tant elements for forging learning methods on graphs (such as Graph Neural Networks). The talk presents the state of the art in the field, starting with basic principles and early work involving Fourier transforms for graph data, covering the main advances and then some connections with machine learning and graph neural networks. In so doing, the presentation also illustrates how Fourier’s approach remains modern and univer sal, and shows how his ideas, derived from physics and enriched by mathematics, computer science and signal theory, remain essential for meeting current challenges in data science. 
10h3012h  Olivier Lézoray  Signal processing on graphs : from images to arbitrary graphs
In this presentation we will look at the adaptation of signal and image processing methods to graphs. Despite their ubiquities, these methods are restricted to signals defined on Euclidean domains. After presenting the principle of no local image filtering, which can be interpreted as filtering on a grid graph using a Laplacian operator, we will present discrete graph computation. The latter provides a set of definitions of differential operators (gradient, divergence, pLaplacian) that can be used to adapt the solution of many inverse problems to graphs. We will present examples for the regularisation of signals from the nodes of an arbitrary graph and their hierarchical decomposition for their manipulation/enhancement. We will then show how mathematical morphology (algebraic or continuous) and its two fundamental operators, dilation and erosion, can be adapted to graphs. Finally, we will show how active contours can be adapted to graphs for segmentation and clustering. 
Lunch Time  
13h3014h30  Hugo Raguet  Signal regularity on graphs and efficient spatial analysis
Problems involving a spatial component can often be solved by minimising a function defined on a graph, whose solutions exhibit a structured form of sparsity. We present the Cut Pursuit algorithm, which exploits this property to efficiently minimise functions with varying degrees of regularity and continuity. Applied to problems whose solution is spatially regular, the Cut Pursuit algorithm achieves a significant speedup and provides convergence guarantees that are independent of the regularity or convexity of the minimised functions. Next, we present the Superpoint approach to analyse largescale 3D data. This method exploits the spatial regularity of the semantic labels of 3D points by partitioning them into simple shapes. The graph encoding the adjacency between these shapes makes it possible to analyse their relationships rather than the individual points, thereby reducing the complexity of the 3D analysis by several orders of magnitude. This method considerably reduces the complexity of 3D analysis while maintaining high accuracy and using fewer computing resources. 
14h4515h45  Julie Coloigner  Signal processing on graphs to interpret and decode brain activity by integrating connectivity
The human brain is made up of a complex network of tens of billions of neurons, each connected to 100,000 others via the axons that make up the fiber bundles of the white matter. Over the past decade, neuroimaging projects such as the Human Connectome Project (HCP) have proposed mapping these brain connections, known as the ’connectome’, to improve our understanding of the functional and structural organization of the brain. Connectivity can be represented in the form of a graph in which the nodes represent brain areas and the edges symbolize the connections between them. Undirected brain connectivity has been classified into two categories : (i) structural connectivity estimated by diffusion MRI (dMRI), where links represent axons or the density of neuronal fibers, and (ii) functional connectivity (measured for example with fMRI, EEG or NIRS), where links represent statistical dependencies between brain signals from different regions, such as correlations, coherence or transfer entropy. In these contexts, robust estimation of these networks remains a challenge. Conventional approaches to estimate connectivity do not exploit all the information in the imaging data, which could be useful for highlighting these complex modifications. The introduction of innovative quantitative imaging techniques for estimating microstructure and multimodality for estimating finer, more specific biomarkers could lead to better estimation in these complex cases. Despite the considerable progress made in this field, relatively few methods exploit the topology of brain networks estimated by structural connectivity to analyse functional brain activity. Some studies have proposed graph signal processing (GSP) approaches, which decomposes brain activity onto the spectral components of the graph. In this presentation, we will give an overview of work in the literature that uses GSP to decode and interpret brain activity. We will also describe in more detail two works in progress. To characterize how signals interact with underlying brain networks, Fourier and wavelet transforms have been extended to graph data. In the first study, we analyse the coupling and decoupling of functional activity on a structural connectivity graph and these measures are then used to classify different diseases such as depression and anxiety. In the second study, we present the advantages of using graph wavelet packets in neuroimaging. We will show the advantages of this approach compared with conventional methods in the context of psychiatric illnesses. 
16h18h  Practical with PyGSP 
Friday 28th Juin: Machine learning on Graphs
Schedule  Speaker  Title & Abstract 

8h3010h  Benoit Gaüzère & Marc Lelarge  Introduction to the representation learning on graphs
Graphs are a versatile representation that simultaneously encodes data and their relationships. Defining Machine Learning models using data in graph form would allow you to take full advantage of their representational power. Unfortunately, the definition of efficient learning models on graphs is not trivial, particularly due to the intrinsic complexity of this type of structure. During this introduction, we will present the different existing approaches to solve this scientific problem : graph embedding, kernels on graphs, edit distance and neural networks on graphs. 
10h10h30  break  
10h3012h  Benoit Gaüzère & Guillaume Renton  Practical on Machine learning on graphs (Pytorch geometric)
The second part of this morning will be devoted to the implementation of machine learning models operating on graphs, with an application to chemical molecules. This tutorial provides a concise introduction to graph neural networks (GNNs) using PyTorch Geometric, a library extending PyTorch for processing graphstructured data. After a guided installation, the tutorial covers key concepts such as datasets and graph convolutions. Through the creation of a simple GNN model for classification, it explains data loading, model definition and training. Case studies illustrate the application of GNN to realworld problems, and advice on model optimisation and eva luation is provided. The tutorial concludes with resources for further learning about GNN and PyTorch Geometric, aimed at equipping readers for their own research or development projects. 
12h14h  Pause  
14h0014h45  Pasquale Foggia  Graphs and machine learning: An overview.
In this presentation, we propose an overview of the most common approaches used in machine learning, with a perspective on the recent contributions from the MIVIA group, university of Salerno. 
14h4515h30  Linlin Jia 
Bridging Graph Spaces: Novel Algorithms and Their Applications Over the years, numerous graphbased machine learning algorithms have emerged. Yet the connections among these methods and the respective spaces remain unrevealed, keeping the underlying theories and potential applications behind the mist. This talk explores the intricate relationships among diverse graphbased machine learning methods and their associated metric spaces, including graph kernels, Graph Edit Distances (GEDs), and Graph Neural Networks (GNNs). The objective is to bridge the existing disparities within these graph spaces while simultaneously enhancing the performance of the underlying methodologies. To achieve this, we introduce two distinct yet interrelated pathways. The first path delves into the effort to refine graph metrics via embeddings across different spaces. The second path explores on the potential of learning graph metrics via GNNs. Beyond these methodologies, we extend our study to the practical applications of distinct graphbased models, with a focus on molecule properties prediction, leveraging recent advances in quantum chemistry. 