Network Seminar

About the Network Seminar

Since December 2018, we are organizing along with Liubov Tupikina a network seminar series at CRI Paris. Talks showcase the use of network science in a wide range of disciplines, from physics to mathematics, to archaeology and biology, to social sciences or neurosciences,and are intended for a broad, interdisciplinary audience.

Below you can find the past and upcoming speakers. Please contact us if you know someone working with networks who would be a great speaker for this event! Talks are 45 min + 15 min questions, and are usually held on Thursdays at 5pm in room 5.11 at CRI Paris, 8bis rue Charles V 75004 Paris.

For description of upcoming events and the latest updates please subscribe for our google-group.

Do not hesitate to write to us at:
marc.santolini at cri-paris.org
liubov.tupikina at cri-paris.org

Seminar series

5 Dec 2019
Stefania Rubrichi (Orange Labs)
TBA

28 Nov 2019
Blaise Delmotte(CNRS Researcher, LadHyX, Ecole Polytechnique)

Leveraging Collective Effects in Active Fluids
We have all witnessed the flocking of starlings in the sky and the schools of fish that form in the ocean. This kind of organization of living systems is not limited to those that we see, but also occurs for those that we don’t: swimming micro-organisms. Collections of micro-swimmers, such as bacteria or sperm cells, exhibit a rich dynamics. They can form coherent structures due to collective motion, mix the surrounding fluid or tune some of its effective physical properties. These fascinating phenomena raise a fundamental question: how can a living system self-organize without any sensorial or cognitive functions ?

Beyond the fundamental interest in the physical understanding of such “active fluids”, their precise modeling is also likely to have applications to the design of new fluid systems with synthetic controllable properties. More recently, artificial micro-swimmers have been developed to mimic these behaviors, but also to achieve specific functions, such as targeted drug delivery or transport in microfluidic systems. 

Active matter therefore offers promising perspectives for the design of smart meta-materials, but also for miniaturized and medical technologies.

In this talk, I will show how to combine experiments, numerical modeling and theoretical calculations to explore different ways to use and control biologic and synthetic micro-swimmers for self-assembly, enhanced fluid mixing and particle transport at the microscopic level. 

17 Oct 2019
Brennan Klein (Network Science Institute, Northeastern University)

The structure is the story: How the right representation can bring forth new theories in complex systems

Abstract: The effectiveness of a network at modeling a given system is intrinsically linked to its ability to express potential or counterfactual interactions in the system. If these neurons fired, would you observe a certain behavior? If I start a rumor, would you hear about it? This ability to encode counterfactual information in their structure makes complex networks rich philosophical objects as well. In this talk, I explore the role—and power—of representation in science and, in particular, complex systems science. To do this, I will primarily focus primarily on two recent projects about representation in network science. First, I will describe a general framework for coarse-graining complex networks to their most informative scale, a phenomenon known as causal emergence. Second, I will describe recent work that compares dozens of tools that are commonly used to infer network structure from time series data. This project is the culmination of a months-long “Collabathon” based out of the Network Science Institute at Northeastern University, and it features contributions from 33 different network scientists from around the world. I will close by briefly describing the structure of this style of research, encouraging more of these large, rigorous, and fun collaborations.

Bio: Brennan is a fifth-year PhD student studying surprise in complex systems. He is focused on understanding how complex systems are able to represent, predict, and intervene on their surroundings across a number of different scales—all in ways that minimize the surprisal experienced in the future. This approach is used to study a range of phenomena from decision making, to experimental design, to causation and emergence in networks. He is currently working with Alessandro Vespignani on a dissertation examining the teleology of networks, or why there appears to be an apparent purpose or goal-directedness to the dynamics and structure of networks. Brennan received his BA in Cognitive Science and Psychology from Swarthmore College in 2014, where he studied the relationship between perception, action, and cognition. He makes art under the pseudonym JK Rofling.

10 Oct 2019
Ray Rivers (Imperial College London)
Myth and Math: A network approach to Atlantis

Abstract: The Aegean archaeologist Carl Knappett, Tim Evans and myself have used network models to try to understand the effect of the eruption of Thera on the demise of the MBA Minoan thalassocracy. The destruction of Thera is often taken to be the basis for the Atlantis legend in popular culture (see D.Duck). I have been fascinated (and appalled) by the continuing potency of this myth, particularly with the European Alt-Right (Google ‘aryan atlantis’ to see). In this talk I will argue that disaster myths have their contradictions and reconsider Thera. I shall also briefly put this network modelling in the context of networking of text-based myths and stories.

Bio: Ray Rivers is Emeritus Professor in Theoretical Physics and a member of The Centre for Complexity Science at Imperial College London. However, in addition to continuing to work on fundamental quantum physics, for the last decade and more he has been active in archaeological network modelling, particularly of the Bronze Age Aegean. Most recently he is concerned with more epistemic issues such as how we impose the present on the past.

3 Oct 2019
Elizabeth Vergu (INRA)
Data analysis and model-based indicators of nodes criticality for epidemic spread and control on a cattle trade network

Informing prevention and control of infectious diseases in livestock populations at regional level necessitates the investigation of the underlying structure of spread represented by animal trade network and the coupling of intra-herd infection dynamics. Despite an abundant literature about dynamical epidemics on contact networks, generic tools are still needed when nodes are subpopulations, to characterize the impact on the infection dynamics of the interplay between network structure and local demography.

In this talk, I will first show some results from the analysis of the French cattle movement network over several years to investigate the fidelity over time of transaction partners. Proxies for pathogen spread, such as reachability ratio, accounting for network time-varying properties, were also computed, using efficient algorithms of network exploration, to assess spread and control of epidemics for various assumptions. In the second part, I will present theoretical results based on a model coupling metapopulation and epidemic dynamics on an explicit network and applied to the French data on cattle trade to build and assess graph vulnerability indicators and explore control strategies.

26 Sept 2019
Vincent Hakim (ENS)
Puzzles about long term memory and neuronal learning
Synapses are important biological structures that serve to transmit information between neurons and are thought to be the sites of learning and memory. Yet, it has remained enigmatic how memory can be retained for years while synaptic components turnover over the course of hours. Similarly, when learning a complex task, the received feedback seems most often global and poorly informative. It is then quite unclear how the strengths of different synapses belonging to numerous different neurons can be properly adjusted, the so called « credit assignment problem ». After recalling some element of synapse biophysics and relevant experimental findings, I will discuss our recent work in collaboration with the teams of A Triller and B Barbour at IBENS, aiming at addressing these two puzzles. 
Bio: Vincent Hakim holds a CNRS Research Director position at Ecole Normale Supérieure (ENS) in Paris, France. He is a theoretical physicist specialised in statistical physics and nonlinear dynamics. During the last twenty years, he has carried out theoretical work in theoretical neuroscience and developmental biology, much of it in close collaboration with experimental teams.

12 Sept 2019
Federico Battiston (CEU)
New emergent behavior in generalized networks
Abstract:
Networks constitute the backbone of many complex systems, on top of which collective behavior can emerge: from epileptic seizures in the human brain to the viral spread of rumours in a social network. When are simple network representations not enough? In this talk I introduce the topic of generalized networks, richer architectures which allow to consider the temporal and multiplex dimensions of relationships, and to go beyond simple pairwise interactions. Building on my contributions to the field, I focus on how to measure multiplexity in real-world systems from the micro to the macro scale, with examples from social, transportation and biological networks. I will then provide a short perspective on dynamical processes on generalized networks, searching for new emergent collective behavior and warning against overly optimistic statements associated to the multiplex, temporal and non-dyadic nature of interactions.

Bio:
Federico is an Assistant Professor at the Department of Network and Data Science at Central European University. Federico studies the structure and the dynamics of complex systems, using his background as statistical physicist (B.Sc. and M.Sc. from Sapienza University of Rome) to look into biological problems, model social systems, and find new solutions for the design of man-made networks. He holds a PhD in Applied Mathematics from Queen Mary University of London, where he was a member of the Complex Systems and Networks Group and worked under the supervision of Vito Latora as part of the EU-FP7 project LASAGNE on multilayer networks. Federico is an elected member of the council of the Complex Systems Society and a former Chair of the Young Researchers of the Society. Before joining DNDS, Federico held postdoctoral positions at the Brain & Spine Institute in Paris, and at the Department of Anthropology at University College London.

14 July 29
Jennifer Dunne (Santa Fe Institute)
Examining human-centered ecological networks across the world

Abstract: Some of our best opportunities for generalizing and enhancing ecological understanding come from studies of ancient ecosystems. One frontier of such research focuses on ecological networks. In this talk, I’ll take a deep-time perspective on food web structure and dynamics. First I’ll briefly discuss the organization of food webs from ecosystems 100s to 10s of millions of years old. Second I’ll discuss a new research agenda to study archaeo-ecological networks from the last several thousand years that include pre-industrial humans. These types of deep-time data sets and analyses provide important perspectives on ecosystem stability, robustness and sustainability as we move further into the Anthropocene.

Bio: Jennifer Dunne is the Vice President for Science at the Santa Fe Institute, where she has been on the faculty since 2007. Dunne has degrees in philosophy (A.B., Harvard), ecology and systematic biology (M.A., San Francisco State University), and energy and resources (Ph.D., University of California, Berkeley). Her research interests are in analysis, modeling and theory related to the organization, dynamics, stability and function of ecological networks at multiple spatial and temporal scales. Dunne was named a Fellow of the Ecological Society of America in 2017. Her publications have appeared in journals such as Proceedings of the National Academy of Sciences USA, Trends in Ecology and Evolution, PLoS Biology, Philosophical Transactions of the Royal Society B, Proceedings of the Royal Society B, Ecology Letters, Ecology, and Ecological Monographs. Media outlets including Scientific American, Wired, SmartPlanet, ScienceNow, Nature News, and Quanta have reported on Dunne’s research.

27 June 2019
Daniele Marinazzo (Ghent University, Belgium):
Synergy and Redundancy: a Network Perspective  

Synergy and redundancy are ubiquitous yet elusive concepts. Frameworks rooted in information theory allow to quantitatively define them, in particular quantifying the joint information shared by two drivers on a target. In this contribution, we will add networks to the picture in three aspects:

1. Synergy and redundancy in building networks: higher order interactions are often ignored when computing dynamical interactions in multivariate systems. I will try to convince you that this is both necessary and informative.

2. Synergy and redundancy as networks: defining a pairwise synergy index we can define networks in which the nodes are pairs of drivers, and the links determine whether they share joint information on a given target. The resulting network has a hierarchical structure, and we can see shared information mapped on graph theory quantities.

3. Synergy and redundancy between networks: we address the information transferred between networks, in a directed way, or mediated by another network. We will present a simple theory and two simple applications, to social media, and neural data.  

20 June 2019
Charles Baroud (Ecole Polytechnique, Pasteur, France).
Abstract: In this presentation I will discuss our work on 3D cell culture in 
droplet microfluidics. I will begin by explaining the technique and how 
it allows us to make, manipulate, and observe hundreds spheroids or 
organoids in parallel. Then I will show some new biological insights we 
have gained by generating very large sets of single-cell data within 
model organoids. Finally, I will describe some recent work to understand 
the interactions between immune cells and cancer models, and how network 
theory can help us understand the biology of the 3D spheroids.

Bio:Charles Baroud studied physics and mechanical engineering at MIT then U. 
Texas at Austin. In 2001 he joined the Laboratoire de Physique 
Statistique at ENS as a post-doc, working on reaction-diffusion 
experiments. Since 2002 he has held a faculty position at Ecole 
Polytechnique, where he has worked on different problems in 
microfluidics, first covering the physical aspects and more recently 
working on biological applications. Since 2018 he has also led a 
research unit at Institut Pasteur, focusing on the use of droplet 
techniques to learn new biology.

13 June 2019
Henry C. W. Price (Imperial College, Londong)
Title: From Local to Global: Nested Interaction and Community in Late Bronze Age Crete. A network approach. 

Abstract: Using as series of null models, we attempt to reconcile complex systems and the simple models that come to characterize much of the explained distribution of artifacts and settlement relationships to the wider Mediterranean. We also try and describe when these models are inappropriate.

Bio: Henry C. W. Price is currently a doctoral candidate in Theoretical Physics at Imperial College London. Working on a diverse number of topics in network theory and applied mathematics. Mainly Complexity Science, Complex Networks and Directed Acyclic Graphs.
Actively researching Cryptocurrency and distributed ledger technology with University of London research groups and others bodies, he also works on machine learning and data science projects in Reg/Fin-tech. 
With an undergraduate degree in Theoretical Physics at Imperial. Henry also studied Mathematics and Financial Modelling and wrote his MSc thesis on Volatility. His most recent research also concerns computational methods in the digital humanities, social and life sciences, in addition to derivatives and futures contracts on Bitcoin.

6 June 2019
Janet Bennion (Vermont University
Title: Polyamory in Paris: A Social Network Theory Application 

Abstract: This research applies social network theory to the polyamory networks (polycules) of Paris, where participants in multiple loving relationships (both partners and metamours) share an exchange of information, ideas, and an assortment of valued prestations such as sexuality, friendship, and money. In such non-monogamous networks, there exists a vast web of nodes connected in much more intimate and complex ways than one finds in the mononormative landscape. This study seeks to evaluate the specific social capital exchanged by 45 individuals in the Polyamour/Polyamorie Paris Facebook group, assessing the strengths and weaknesses of their ties, and better understanding how they navigate the web to simultaneously live intimately with others and still fulfill their own selfish needs and desires.

Bio: Janet Bennion is a Professor of Anthropology from Northern Vermont University. She is the preeminent scholar on polygyny among Mormon Fundamentalist groups, having published extensively on female networking and the variability of poly lifestyles, including Women of Principle (University of Oxford Press, 1998) and Polygamy in Primetime (Brandeis University Press 2012). Her latest publication is collaborative and international, stemming from several international conference discussions about how to handle the poly world legally, compiled in the volume, The Polygamy Question (Utah State and Colorado Presses, 2015). Her most recent research explores polyamory networks in Paris where she is testing the efficacy of network theory on 45 subjects involved in 24 polycules.

23 May 2019
Guillaume Dumas (Pasteur Institute):
Title: Networks and Machine Learning in Biomedical Research: from Generative Models in Social Neurosciences to Multi-Scale Priors in Precision Medicine

With the recent development of –omics fields, multiple sub-disciplines in life sciences have embraced networks as a valuable formalism. This presentation will present two projects illustrating how –omics networks can be used in biomedical research: 1) whole brain neuro-computational modelling with Connectomics, and 2) network-based stratification (NBS) with Genomics. The first example builds on the importance of anatomical and functional interactions in neuroscience; through modelling, numerical simulation provides insight into the basic mechanisms that enable integrative neural processes and how structural brain networks generate spatially and temporally organised brain activity at both intra- and inter-individual levels. The second example was originally designed for cancer research; the NBS combines genetic mutation profiles of patients with protein-protein interaction (PPI) networks to uncover clusters of patients with similar tumour subtypes. Here we will review the original results through a case study of reproducibility in bioinformatics and then explain the challenges to apply it to autism.

Guillaume Dumas is a researcher in the department of neuroscience of the Institut Pasteur in Paris. Originally from engineering and theoretical physics, he did a Ph.D. on cognitive neuroscience at the University of Paris 6 (UPMC) and then moved in a postdoc at the Center for Complex System and Brain Science of Florida Atlantic University. He came back to France for working in the “Human Genetics and Cognitive Functions” unit of the Institut Pasteur, where he started as a postdoc before receiving his permanent position. He then created the platform SoNeTAA (Social Neuroscience for Therapeutic Approaches of Autism) at the child and adolescent psychiatric department of the Robert Debré hospital in Paris. His interdisciplinary research is focused on integrative accounts of neural, behavioural and social coordination dynamics. Methods used range from both intra- and inter-individual neuroimaging techniques to neurocomputational simulations. He is also a science writer and journalist and is engaged with multiple projects at the cross-road of Art and Science. He has, for instance, co-founded HackYourPhD, a community advocating internationally the use of openness in Science and Knowledge as a common good, and ALIUS, an international and interdisciplinary research group dedicated to the investigation of the diversity of consciousness.

18 April 2019
Alexey Medvedev (Université de Namur and Université catholique de Louvain):
Title: Predicting discussion dynamics in online forums using Hawkes processes: case study of Reddit

Online forums provide rich environments where users may post questions and comments about different topics. Understanding how people behave in online forums may shed light on the fundamental mechanisms by which collective thinking emerges in a group of individuals, but it has also important practical applications, for instance to improve user experience and increase engagement. Communication in online forums mostly happen through the open discussions, potentially available for all members of a particular platform. Cascading patterns, often emerging and well studied in online social media, are represented in forums in the form of threads, or, otherwise called, discussion trees. The two main questions arise: what is the shape and what is the dynamics of evolution of these trees? In this talk I will present an overview of the main research directions that arose in recent years and focus primarily on the most popular platform, Reddit. Then I will present a model of discussion trees generation based on the self-exciting Hawkes processes, which represents both the tree structure and temporal information. This model was applied to Reddit discussion trees and showed better accuracy in predicting future flow of the discussion, in comparison with the contemporary dynamic cascade models.

28 Feb 2019
Chiara Poletto (INSERM):
Title: Accounting for variable and heterogeneous human behaviour in the assessment of an epidemic

Mathematical and computational approaches based on network theory and complex system dynamics are becoming increasingly effective in tackling open problems in infectious disease epidemiology. I will review my recent research work in this direction presenting studies on both fundamental problems and specific epidemic events. On the theoretical side, I will show how the structure and dynamics of human-to-human contacts alter the risk of an outbreak. This effect can be captured by the epidemic threshold, the critical transmissibility below which extinction is certain, that quantifies the vulnerability of a population to an infection. In order to compute this indicator we developed the infection propagator approach, where we used the multilayer framework to describe a temporal network and the Markov-chain formalism to solve the spreading dynamics in the critical regime close to the threshold. On the applicative side I will show how health seeking behavior, daily social contacts, and daily mobility patterns affect the initial transmission of an emerging disease and the propagation dynamics of recurrent epidemics at the city and regional level. These understandings were important for the epidemiological assessment of the MERS global dissemination, the recent outbreak of Zika and the seasonal influenza circulation.

Bio: Chiara Poletto is a researcher at the National Institute of Health and Medical Research (INSERM) in Paris (France), working on the spreading of infectious diseases seen as a complex system phenomenon. Epidemics are mediated by human contacts and, on a different scale, by human mobility patterns. Therefore, the role of network structure on infection risk, its persistence and impact on the population is a central question of her research work. Within this broad context, she is dedicating increasing attention to the physics of interacting spreading processes underlying important problems in disease ecology. Poletto received her PhD in Physics from the University of Padova (Italy) in 2009 and was then Post Doc at the Computational Epidemiology Laboratory, ISI Foundation, Torino (Italy), before joining the INSERM in 2012. She is recipient of the Junior Scientific Award of the Complex Systems Society for extraordinary scientific achievements, and of the starting grant from the program Emergence de la Ville de Paris. Her studies on emerging pathogens’ epidemics (from Ebola outbreak to Zika) have translated into expert advices for public health decision makers.

6 Dec 2018
Christian Vestergaard (Pasteur Institute)
Title: Randomized reference models for temporal networks

Many complex systems can successfully be described as networks. This abstracts away most particularities of individual units in the system and allows to highlight and study the effects of the complex structure of their connections. Notable examples are human interaction networks and the spread of contagions and information in these, biological networks, including biotic interactions in ecosystems and neural connections in the brain, infrastructural networks, and world trade. The structure and dynamics of most empirically measured networks are complex and intrinsically correlated, making it particularly challenging to study them using traditional generative models.

As an alternative to the bottom-up approach of generative models, randomized reference models (RRMs) constitute a top-down approach to studying complex networks. RRMs deal with the controlled destruction of given temporal or topological structures in a complex network in order to create a distribution of reference networks retaining certain features of the original network. They are typically implemented as procedures that shuffle the edges of the network while retaining the features in question. This makes RRMs very generally applicable, and in particular in cases where we are not able to specify a realistic generative model. However, the effects of most shuffling procedures on network features remain poorly understood, complicating the interpretation of results, and the lack of a common framework has made it difficult to compare different RRMs.

I will describe a unified framework for the important class of RRMs generated by uniform shuffling procedures, which we by analogy to statistical physics will name microcanonical RRMs (MRRMs). Our framework lets us build a taxonomy of MRRMs that orders them and deduces their effects on important network features. It additionally tells us how we may generate new MRRMs by combining existing ones. I will discuss how MRRMs may be used as null models to identify statistically significant features in empirical networks. I will show how our framework can be used when we do not know how to choose the correct null model, and will apply this to characterize computational motifs in the Drosophila larval brain. I will finally show how series of different MRRMs may be used to pick apart the effects of different features of a network on dynamical processes unfolding on it.

Reference: https://arxiv.org/pdf/1806.04032.pdf