Invited speakers

Luca Aiello

Research Scientist at Yahoo Labs, London, got his PhD in Computer Science from the University of Torino, Italy in 2012 and he has been a Research Scientist at Yahoo Labs for 4 years, first in Barcelona and now in London. He conducts interdisciplinary research in social media, computational social science, and urban informatics through quantitative analysis of big data. At Yahoo he works with production teams of Flickr and Tumblr to build social recommender systems at scale. He has collaborated actively with several academic institutions worldwide including Indiana University, University of Southern California, KAIST, and more recently Stanford University in conjunction with whom he organized the Social Informatics conference in Barcelona in 2014.

Johan Bollen

Johan Bollen is associate professor at the Indiana University School of Informatics and Computing. He was formerly a staff scientist at the Los Alamos National Laboratory from 2005-2009, and an Assistant Professor at the Department of Computer Science of Old Dominion University from 2002 to 2005. He obtained his PhD in Experimental Psychology from the Vrije Universiteit Brussel (VUB) in 2001. He has published more than 75 articles on computational social science, social media analytics, informetrics, and digital libraries. His research has been funded by the NSF, IARPA, and Andrew W. Mellon Foundation. Johan lives in Bloomington, Indiana with his wife and daughter. In his free time he enjoys P90x and DJing in the local Bloomington clubs as DJ Angst (with his colleague E-trash aka Luis Rocha).

Vittoria Colizza

Dr. Vittoria Colizza is a permanent senior researcher at Inserm (French National Institute of Health and Medical Research) & Universite Pierre et Marie Curie, Faculte de Medecine, Paris, France. She also has a joint appointment at ISI Foundation, Turin, Italy. She completed her undergraduate studies in Physics at the University of Rome Sapienza, Italy, in 2001 and received her PhD in Statistical and Biological Physics at the International School for Advanced Studies (SISSA) in Trieste, Italy, in 2004. She then spent 3 years at the Indiana University School of Informatics in Bloomington, IN, USA, first as a post-doc and then as a Visiting Assistant Professor. In 2007 she joined the ISI Foundation in Turin, Italy, where she started a junior lab after being awarded a Starting Independent Career Grant in Life Sciences by the European Research Council Ideas Program. In 2011 Vittoria joined the Inserm in Paris where she now leads the EPIcx lab (Epidemics in complex environments, within the Pierre Louis Institute of Epidemiology and Public Health (UMR-S 1136).
Her research activities are based on sophisticated models and massive empirical datasets aimed at producing advances in the surveillance, modeling and prediction of epidemic spread. They are further supported by theoretical approaches integrating methods of complex systems, network science and statistical physics aimed at underpinning the mechanisms behind observed spreading phenomena. In addition to the ERC Starting Grant in 2007, Colizza received the Young Talent Award by the Italian Ministry of Youth in 2010, the Prix Louis-Daniel Beauperthuy 2012 (Human biology & Medical sciences) by the French Academy of Sciences, the Young Scientist Award for Socio-Econophysics in 2013. She is Member of the Steering Committee of the Complex Systems Society Conference, Advisory Board Member of the WHO Collaborative Center ‘Complexity Science for Health Systems’ (CS4HS), Advisory Board Member of the AXA Research Funds Funding Program. She served as the Young Advisor to the Vice President of the European Commission Mrs. Neelie Kroes for the new Digital Agenda for Europe in 2011-2014, and as Advisory Board Member of the FET Young Explorer Funding Program for the FP7 Framework of the European Commission.

Tina Eliassi-Rad

Tina Eliassi-Rad is an Associate Professor of Computer Science at Northeastern University in Boston, MA. She is also on the faculty of Northeastern's Network Science Institute. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is rooted in data mining and machine learning; and spans theory, algorithms, and applications of massive data from networked representations of physical and social phenomena. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, and cyber situational awareness. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2010, she received an Outstanding Mentor Award from the Office of Science at the US Department of Energy. For more details, visit

Viktor Jirsa

Viktor Jirsa is Director of the Institute de Neurosciences des Systèmes and Director of Research at the CNRS. Since the late 90s he has made contributions to the understanding of how network structure constrains the emergence of functional dynamics using methods from nonlinear dynamic system theory and computational neuroscience. Dr. Jirsa has been awarded several international and national awards for his research including the Early Career Distinguished Scholar Award in 2004 and the Francois Erbsmann Prize in 2001. He serves on various Editorial Boards and has published more than 80 scientific articles and book chapters, as well as co-edited several books including the Handbook of Brain Connectivity

Yamir Moreno

Prof. Yamir Moreno got his PhD in Physics (Summa Cum Laude, 2000) from University of Zaragoza. Shortly afterwards, he joined the Condensed Matter Section of the International Centre for Theoretical Physics (ICTP) in Italy as a research fellow. He is the head of the Complex Systems and Networks Lab (COSNET) since 2003 and is also affiliated to the Department of Theoretical Physics of the Faculty of Sciences, University of Zaragoza. He is the Deputy Director of the Institute for Biocomputation and Physics of Complex Systems (BIFI) and member of its Government Board and Steering Committee. During the last years, he has been working on multilevel complex systems and the structure and dynamics of networks. Prof. Moreno’ scientific production amounts to 145+ peer-reviewed publications with a total of 8900+ citations (h-index=39, as of August, 2015). He has been the PI of several Regional/National/International Grants, and has supervised (is supervising) 6 (4) PhD Thesis. Currently, Prof. Moreno is an Editorial Board Member of Scientific Reports (NPG), PLoS ONE, Journal of Complex Networks, Applied Network Science and a Divisional Associated Editor of Physical Review Letters, President of the Complex Systems Society, Member of the FET Advisory Group for the H2020 Framework of the EU Commission, an Advisory Board Member of the WHO Collaborative Center “Complexity Sciences for Health Systems” (CS4HS), Secretary of the Network Science Society and an ISI Foundation Fellow.

Daniela Paolotti

Daniela Paolotti got her PhD in Physics from the University of Camerino, Italy in 2005. She is Research Leader in the Computational Epidemiology and Public Health group at ISI Foundation in Turin. Before joining ISI in 2008, she was a researcher in the Bioinformatics group at the Novartis Vaccine Research Center in Siena. Her main interest is in digital and computational epidemiology. At ISI she coordinates a network of platforms for web-based surveillance in ten European countries called Influenzanet.

Foster Provost

Foster Provost is Professor of information systems and NEC Faculty Fellow at New York University's Stern School of Business. Professor Provost has a Bachelor of Science from Duquesne University in physics and mathematics and an Master of Arts and Ph.D. in computer science from the University of Pittsburgh. Professor Provost is known for his work on evaluating machine learning algorithms using ROC analysis, and for his work on social network analysis for targeted marketing.

Jari Saramäki

Jari Saramäki is Associate Professor of Computational Science at Aalto University, Dept. of Computer Science, Helsinki, Finland. He received his PhD in 1998 in engineering physics, studying low-temperature quantum phenomena, and has subsequently also worked in data mining and telecommunications industries. He was nominated associate professor in computational science in 2012; prior to that he has led the Complex Networks research group at Aalto for many years and has been appointed a visiting scholar at the University of Oxford (fall 2010). He has been one of the pioneers of the temporal networks framework.

Alessandro Vespignani

Alessandro Vespignani received his undergraduate degree and Ph.D., both in physics and both from the University of Rome “La Sapienza,” in 1990 and 1994 respectively. He completed his postdoctoral research at Yale University and Leiden University. Prof. Vespignani worked at the International Center for Theoretical Physics (UNESCO) in Trieste and at the University of Paris-Sud in France as a member of the National Council for Scientific Research (CNRS) before moving to Indiana University in 2004. Before joining Northeastern University Vespignani was J.H.Rudy Professor of Informatics and Computing at Indiana University and serving as the Director of the Center for Complex Networks and Systems Research and the Associate Director of the Pervasive Technology Institute. Vespignani is elected fellow of the American Physical Society, member of the Academy of Europe, and fellow of the Institute for Quantitative Social Sciences at Harvard University. He is serving in the board/leadership of a variety of professional associations and journals and the Institute for Scientific Interchange Foundation.

Invited talks

Luca Aiello (Yahoo, UK)

Social Networks below the Surface: An Exploration of the Nature of Social Processes.

Graphs are powerful abstractions of interactions between entities that has allowed researchers to build rigorous models to describe the evolution of complex systems, especially social networks, and the dynamics of the phenomena that these systems support, such as the process of information exchange. The explosion of data from online social media has encouraged the often uncritical adoption of the notion of social tie as the atomic interaction quantum of any social network structure. However, links are not all created equal and, similarly, the information that spreads in networks is variegated in nature. In our work we aim at reaching a better understanding of the nature of social structures and of the information that is carried along them. We look at how a social network evolves by analyzing full longitudinal traces of link creation in large-scale social platforms, showing how the process of bonding can be biased by algorithmic artifacts (such as friendship recommender systems) that influence the creation of links in a way that can change the global structural properties of the network. Moreover, by operationalizing theories from social science, we provide evidence that links might be expression of very different social processes that reflect fundamentally different structural properties of the social graph. Then, by illustrating two specific case studies (the production of quality content and the diffusion of deviant content) we will show how content type, link types, and overall network structure are all connected and have effect on each another. With our work, we hope to convey the message that a more nuanced description of social structures and processes is needed to better understand human interactions and societies, both online and offline.

Johan Bollen (Indiana University, USA)

Quantifying the effects of online social networks on subjective well-being and mental health

The introduction of social media has had contradictory effects. As a social species we require social relations for our well-being, but recent results indicate that widespread social media use leads to increased feelings of dissatisfaction and reduced happiness. The key to this paradox may lie in the unequal distribution of social relations in social networks and how these distributions affect our collective and individual subjective well-being. In this talk I will discuss the results of our investigations into how large-scale social networks can affect societal happiness levels and how we can leverage social media data to quantify the morbidity and progression of mood disorders in the population.

Vittoria Colizza (INSERM, France)

Vulnerability to infections: applications for risk assessment in infection prevention and control

Our understanding of communicable diseases prevention and control is rooted in the theory of host population transmission dynamics. The network of host-to-host contacts along which transmission can occur drives the epidemiology of communicable diseases, determining how quickly they spread and who gets infected. A large body of epidemiological, mathematical and computational studies have provided a number of insights into the understanding of the process and the identification of efficient control strategies. The explosion of time resolved contact data has however opened the stage to new challenges. What are the structural and temporal aspects, and possibly their non-trivial interplay, that are critical for disease spread? To answer this question, I will introduce the infection propagator approach, a theoretical framework for the assessment of the degree of vulnerability of a host population to disease epidemics, once we account for the time variation of its contact pattern. By reinterpreting the tensor formalism of multi-layer networks, this approach allows the analytical computation of the epidemic threshold for an arbitrary time-varying network of host contacts, i.e. the critical pathogen transmissibility above which large-scale propagation occurs. I will apply this framework to a set of empirical time-varying contact networks and show how it can be used to test different intervention strategies for infection prevention and control in realistic settings.

Tina Eliassi-Rad (Northeastern University, USA)

Sifting through Measures on Networks: From a Theoretical Framework to an Empirical Guide

In this talk, I will discuss two problems on network data. (1) Measuring tie-strength: Given a set of people and a set of events attended by them, how should we measure connectedness or tie strength between each pair of persons? The underlying assumption is that attendance at mutual events produces an implicit social network between people. I will describe an axiomatic solution to this problem. (2) Measuring similarity between networks: Given two networks (without known node-correspondences), how should we measure similarity between them? This problem occurs frequently in many real-world applications such as transfer learning, re-identification, and change detection. I will present an empirical guide on how to select a network-similarity method.

Viktor Jirsa (Aix-Marseille University, France)

The Virtual Brain: biologically realistic network modeling merging structure and dynamics

Over the past decade we have demonstrated that the fusion of subject-specific structural information of the human brain with mathematical dynamic models allows building biologically realistic brain network models, which have a predictive value, beyond the explanatory power of each approach independently. The network nodes hold neural population models, which are derived using mean field techniques from statistical physics expressing ensemble activity via collective variables. This approach has been successfully applied to the modeling of the resting state dynamics of individual human brains, as well as clinical situations including stroke and epilepsy research. In epilepsy, we reconstruct personalized connectivity matrices of human epileptic patients using Diffusion Tensor weighted Imaging (DTI). Subsets of brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other brain regions and propagate activity through large brain networks, which comprise brain regions that are not necessarily epileptogenic. The identification of the EZ is crucial for candidates for neurosurgery and requires unambiguous criteria that evaluate the degree of epileptogenicity of brain regions. Stability analyses of propagating waves provide a set of indices quantifying the degree of epileptogenicity and predict conditions, under which seizures propagate to nonepileptogenic brain regions, explaining the responses to intracerebral electric stimulation in epileptogenic and nonepileptogenic areas. We demonstrate the predictive value of our seizure propagation model by validating it against empirical patient data.  In conjunction, our results provide guidance in the presurgical evaluation of epileptogenicity based on electrographic signatures in intracerebral electroencephalograms.

Yamir Moreno (University of Zaragoza, Spain)

Contagion Processes on Multilayer Networks

This talk will deal with two of the most representative and studied dynamical processes on networks: disease contagion and information spreading. We will first summarize some basic and fundamental results concerning disease and information spreading in single-layered networks. Then, we will increasingly add complexity to the models, in the form of more sophisticated analyses on so called multilayer networks, which are made up by more than one layer and represent more accurately several real-world systems where disease spreading or information contagion/diffusion is most relevant. 

Daniela Paolotti (ISI Foundation, Italy)

Digital traces and participation for forecasting the flu

The ability to rapidly recognize and respond to both global and local health threats remains a critical public health priority. The ever-growing digital world represents an unprecedented opportunity to harvest for tools to face emerging and re-emerging public health issues. This digital means of disease and, more in general, health and behavior-related information detection has been made possible by the growing influence of Internet technology, which has significantly changed the landscape of public health surveillance, epidemic intelligence gathering and forecasting of epidemic spreading. Informal digital channels such as social networking sites, blogs, chat rooms, Web searches, local news media, crowdsourcing platforms have been credited with providing information that are not easily accessible by more traditional channels, such as census or traditional surveillance. We have explored how digital traces left by humans interacting with each other on the web (i.e. geo-located Twitter data used to extract mobility patterns on a Europe-wide scale) and voluntarily provided self reports on health related matters (self reported Influenza-like Illness symptoms to detect in real time and with high geographical resolution the seeds for seasonal influenza outbreaks) can become a crucial tool in informing dynamical models for epidemic spreading on a national and global scale.

Jari Saramäki (Aalto University, Finland)

Social networks, time, and individual differences

In the traditional “bare-bones” network approach, nodes are nodes and links and links, and that is all there is. For social networks, this means that individuals are distinguishable only on the basis of their network characteristics (degree, centrality, etc). However, we all know that people are different and behave in different ways. These differences can be approached with more fine-grained behavioural data, in particular with the help of data on time-stamped interactions that allow constructing dynamic and temporal social networks. In this talk, I will focus on exploring individual differences with the help of temporal data on electronic interactions (calls, emails, etc). I will first talk about longer timescales and the similarities and differences in how we maintain our personal networks. Then, I will focus on shorter timescales of circadian patterns, and show how various data sets reveal chronotypes of individuals (morning/evening-active persons) and chronotype compositions of populations.

Alessandro Vespignani (Northeastern University, USA)

From the physics of reaction-diffusion processes in complex networks to infectious disease forecast

Mathematical and computational methods have gained importance in the public-health domain, especially in epidemic modeling, by providing quantitative analysis in support of the policy-making processes. At the same time, the epidemic modeling metaphor has been introduced to describe a wide array of different phenomena. The spread of information, cultural norms, and social behavior can be all conceptually modeled as a contagion process. Although the basic mechanisms of each phenomenon are different, their effective mathematical description often defines similar constitutive equations and dynamical behaviors framed in the general theory of reaction-diffusion processes. In this lecture I will review the theoretical framework developed in recent years to understand the dynamic of reaction-diffusion processes in expanded schemes that explicitly include the many realistic features that characterize human mobility behavior, and complex interactions patterns. I will also discuss how this framework is at the core of data-driven computational approaches that, through the integration of large-scale data sets and the explicit simulation of entire populations down to the scale of single individuals, are successfully used in the analysis and forecast of real epidemics.


Foster Provost (New York University)

Predictive Modeling with Social Networks

Over the past decade there has been a surge of interest in methods for analyzing complex social networks: from communication networks, to friendship networks, to professional and organizational networks.  For predictive modeling, the dependencies among linked entities in the networks present an opportunity to improve inference about properties of individuals, as birds of a feather do indeed flock together. For example, when estimating whom to target with a product offer, who is likely to leave the organization, who has committed financial malfeasance, and many more aspects of individuals, it often is quite helpful to consider the status of people's friends along these dimensions, and even more interestingly, our predictions about the status of people's friends. This tutorial will explore the unique opportunities and challenges for predictive modeling with social network data. We will begin with a description of the problem setting, including examples of various applications of social network mining (e.g., targeted marketing, on-line advertising, churn prediction, financial malfeasance, fraud detection).  We will then present a number of characteristics of social network data that differentiate them from the traditional settings for inference and learning, and outline the resulting opportunities for significantly improved inference and learning. We will discuss specific techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues and potential modeling pathologies that are unique to network data.
The focus in this tutorial will be to cover the basics, to discuss real applications and results where possible, and to provide a framework for understanding the more advanced concepts.  We also will provide supplemental material on more advanced concepts, including links to the literature.
Prerequisites: The tutorial assumes a basic knowledge of statistical inference, predictive modeling, and machine learning.

List of talks

Contributed talks are of 12 minutes plus questions.

  1. David Angulo-Garcia, Joshua D. Berke and Alessandro Torcini. Cell Assembly Dynamics of Sparsely-connected Inhibitory Networks

  2. Sergio Arregui, Fernando Cid, Joaquín Sanz and Yamir Moreno. Context-specific protein-protein interactions in Mycobacterium Tuberculosis

  3. Federico Battiston, Vincenzo Nicosia and Vito Latora. Rich cores in multiplex networks

  4. Nicole Beckage, Markus Brede and Massimo Stella. Mental Lexicon Growth Modeling Reveals the Multiplexity of the English Language

  5. Mireille Bonnard, Sophie Chen, Jérôme Gaychet, Marcel Carrère, Michael Woodman and Viktor Jirsa. Resting state brain dynamics and its transients explored with combined TMS-EEG

  6. Roland Bouffanais and David Mateo. Influence of interaction network topology on the dynamical response of swarming systems

  7. Giona Casiraghi. A Broad Class of Ensembles for Real World Networks

  8. Nicolas Claidiere, Julie Gullstrand, Aurélien Latouche and Joel Fagot. Using automatized cognitive testing to analyse the social network of baboons

  9. Ruaridh Clark, Giuliano Punzo, Kristaps Baumanis and Malcolm Macdonald. Leader Selection for Consensus Speed Optimisation in Weighted k-NNR Networks

  10. Davide Colombi, David Emmanuel Rivalyn Nakouné Yandoko, Chiara Poletto, Hervé Bourhy and Vittoria Colizza. On the mechanisms for persistence of the rabies virus (RABV): eects of movement network and incubation period heterogeneity

  11. Owen Courtney and Ginestra Bianconi. Generalised Network Structures: The configuration model of simplicial complexes

  12. Emanuele Cozzo, Guilherme Ferraz de Arruda, Francisco Rodrigues and Yamir Moreno. Structural transition in multiplex networks triggered by layer degradation

  13. Matthieu Cristelli. The Heterogeneous Pathways of Development of Nations

  14. Alexandre Darbon, Chiara Poletto, Sara Andraghetti, Eugenio Valdano, Michele Tizzoni and Vittoria Colizza. Competing spreading processes on temporal networks

  15. Manlio De Domenico. Mapping multiplex hubs in human functional brain network

  16. Sarah de Nigris, Anthony Hastir and Renaud Lambiotte. Burstiness and fractional diffusion on complex networks

  17. Aurélien Decelle and Cyril Furtlehner. Generalized Belief-Propagation applied to inverse problems, sampling and optimization on complex network

  18. Juan Fernandez-Gracia, Caroline C. Buckee and Jukka-Pekka Onnela. Flexible network embedding

  19. Julie Fournet and Alain Barrat. Epidemic risk from friendship network data: an equivalence with a non-uniform sampling of contact networks

  20. Mathieu Génois, Christian Lyngby Vestergaard and Alain Barrat. Using co-presence as a proxy for contact networks relevant for spreading processes

  21. Antonia Godoy, Roger Guimera, Marta Sales-Pardo and Cristopher Moore. Accurate and scalable social recommendation using mixed membership stochastic block models

  22. Ronan Hamon, Pierre Borgnat, Patrick Flandrin and Céline Robardet. Networks as signals: Extraction of dynamical network structures

  23. Philipp Hoevel, Florian Fiebig, Hartmut H. K. Lentz and Vitaly Belik. Controling contagious processes on temporal networks via adaptive rewiring

  24. Jacopo Iacovacci, Federico Battiston, Vincenzo Nicosia, Vito Latora and Ginestra Bianconi. Emergence of Multiplex Communities

  25. Gerardo Iñiguez, Márton Karsai, Riivo Kikas, Zhongyuan Ruan, Kimmo Kaski and János Kertész. Global contagion with local cascades: Modelling the slow adoption of technology online

  26. Pablo Jensen, Matteo Morini, Márton Karsai, Tommaso Venturini, Alessandro Vespignani, Mathieu Jacomy, Jean-Philippe Cointet, Pierre Mercklé and Eric Fleury. Detecting global bridges in networks

  27. Janos Kertesz, Janos Török, Yohsuke Murase, Hang-Hyun Jo and Kimmo Kaski. What do Big Data tell? Sampling the social network by communication channels

  28. Moses C. Kiti, Michele Tizzoni, Timothy M. Kinyanjui, Dorothy C. Koech, Patrick K. Munywoki, Milosch Meriac, Luca Cappa, André Panisson, Alain Barrat, Ciro Cattuto and D. James Nokes. Quantifying social contacts in a household setting of rural Kenya using wearable proximity sensors

  29. Tim Kunze, Alexander Hunold, Jens Haueisen, Viktor Jirsa and Andreas Spiegler. Modeling transcranial direct-current stimulation (tDCS) in a large-scale human brain network model

  30. Claire Lagesse. Read Cities through their Lines. How to highlight time in road networks spatiality

  31. Maxime Lenormand, Thomas Louail, Juan Murillo Arias and José J. Ramasco. Crowdsourcing the Robin Hood effect in the city

  32. Yannick Léo, Eric Fleury, José Ignacio Alvarez-Hamelin, Carlos Sarraute and Márton Karsai. Socioeconomic correlations in communication networks

  33. Manuel Sebastian Mariani, Matus Medo and Yi-Cheng Zhang. Early identication of high-quality papers

  34. Manuel Jiménez Martín, Javier Rodriguez Larguna and Elka Korutcheva. Assesing structural balance drive in the evolution of international relationships

  35. Rossana Mastrandrea and Alain Barrat. How to estimate epidemic risk from incomplete contact diaries data?

  36. Francesca Melozzi, Christophe Bernard and Viktor Jirsa. The Virtual Mouse Brain

  37. Dina Mistry, Qian Zhang, Nicola Perra and Andrea Baronchelli. Committed activists and the reshaping of status-quo social consensus

  38. Antoine Moinet, Michele Starnini and Romualdo Pastor Satorras. Aging effects in percolation processes of Non Poissonian temporal networks

  39. Kanchan Mopari, Alessio Cardillo, Paolo De Los Rios, Alex Arenas and Jesus Gomez-Gardenes. To vaccinate or not to vaccinate? A coevolutionary dilemma

  40. Paolo Moretti. The role of hierarchy in the resilience of biological networks.

  41. Sarah Morgan, Sophie Achard and Petra Vertes. Mapping the motif Pareto space of fMRI brain networks

  42. Oliver Mülken. Complex Quantum Networks: From Universal Breakdown to Optimal Transport

  43. Vahan Nanumyan. Inferring Weighted Signed Relations from Repeated Interactions

  44. Geza Odor, Ronald Dickman, Silvio Da Costa Ferreira and Wesley Cota. Burstiness, localization and Griffiths effects in network models

  45. Spase Petkoski, Jean-Jacques Temprado and Viktor Jirsa. The spatial distribution of time delays determines the synchronization of coupled oscillators

  46. Giovanni Petri, Francesco Vaccarino and Demian Battaglia. Homological features of brain functional connectomes show dynamical compensation of age-related structural brain connectomes changes

  47. Chiara Poletto, Eugenio Valdano and Vittoria Colizza. Analytical computation of the epidemic threshold on temporal networks: impact of immunity and latency periods

  48. Ivan Puga-Gonzalez and Cedric Sueur. Social Networks in an individual-based model of primate social behaviour

  49. Filippo Radicchi and Claudio Castellano. Beyond the locally tree-like approximation for percolation on real networks

  50. Jose J. Ramasco. Percolation-based precursors of transitions in spatially extended systems

  51. Victor Ramiro, Emmanuel Lochin and Patrick Senac. Characterization and Applications of Temporal Random Walks on Opportunistic Networks

  52. Marijke Rauch, H. Hildenbrandt and C.K. Hemelrijk. Network analysis of a modelled flock of starlings

  53. Soumen Roy. Novel interdisciplinary applications of network science: From image processing and diagnostics to protein design

  54. Maria Luisa Saggio, Petra Ritter and Viktor K. Jirsa. Analytical Operations Relate Structural and Functional Connectivity in the Brain

  55. Anna Sapienza, Laetitia Gauvin and Ciro Cattuto. Joint Factorization for Analysing Multi-dimensional Data

  56. Fabio Saracco, Riccardo Di Clemente, Andrea Gabrielli and Tiziano Squartini. Detecting the bipartite World Trade Web evolution across 2007: a motifs-based analysis

  57. Matthew Silk, Lucy Steward, Darren Croft, Dave Hodgson, Mike Boots, Dez Delahay and Robbie Mcdonald. Dynamic networks and disease across different timescales in a wild animal

  58. Somdatta Sinha. Protein Contact Networks: Structure, Function and Evolution

  59. Michele Starnini, Mattia Frasca and Andrea Baronchelli. Group segregation and the emergence of metapopulation structures

  60. Massimo Stella, Cecilia Andreazzi, Sanja Selakovic, Alireza Goudarzi and Alberto Antonioni. Parasite spreading in spatial ecological multiplex networks

  61. Eugenio Valdano, Chiara Poletto, Christian Vestergaard, Mathieu Génois, Alain Barrat and Vittoria Colizza. Assessing the impact of spatial and temporal resolution on spreading potential through data-driven analytical and numerical approaches

  62. Toni Vallès-Català, Francesco A. Massucci, Marta Sales-Pardo and Roger Guimera. Multilayer stochastic block models reveal the multilayer structure of complex networks

  63. Christian Lyngby Vestergaard, Mathieu Génois, Eugenio Valdano and Taro Takaguchi. Randomization techniques for the analysis of dynamical processes on temporal networks

  64. Andrea Zaccaria, Matthieu Cristelli, Andrea Tacchella and Luciano Pietronero. How the Network of Products Drives the Economic Development of Countries

List of posters

Posters will be displayed in the coffee break area during the three days of the conference;
expected size is A0 (each display panel is 90cm x 180xm)

  1. Alberto Aleta, Andreia Hisi, Chiara Poletto, Sandro Meloni, Vittoria Colizza and Yamir Moreno. Persistence of rapidly mutating pathogens on a metapopulation network

  2. Joël Amedon, Joël Fagot, Nicolas Claidière.Dynamic social network and cognitive performance: a preliminary study in Baboons (Papio papio)

  3. Satish Badepalli and Murat Yuksel. Universal Power Exponent in Network Models of Thin Film Growth

  4. Federico Botta and Charo I. Del Genio. Modularity density as a quality function for community detection: a detailed analysis

  5. Andrea Brovelli, Jean-Michel Badier, Francesca Bonini, Fabrice Bartolomei, Olivier Coulon, Guillaume Auzias. Dynamic reconfiguration of cortico-cortical and cortico-subcortical functional connectivity mediating visuomotor behaviours

  6. Giulia Cencetti. Control theory of instabilities on complex networks

  7. Nicolás Deschle, Bastian Pietras and Andreas Daffertshofer. Estimation of relaxation times in coupled finite size oscillatory networks.

  8. Francesca Di Patti. Topology driven pattern on directed networks: a multiple-scale approach

  9. Jan Fousek and Eva Výtvarová. Temporal networks of dynamical synchronization in neuroimaging data

  10. Frédéric Guinand and Yoann Pigné. Reachability in Maritime Transportation Networks

  11. Caroline Haimerl, Susanne Reichinnek, Vincent Villette, Rosa Cossart and Arnaud Malvache. Self-referenced spatio-temporal coding in the hippocampus through speed-modulated theta oscillation

  12. Juan Hernandez and Christian Gonzalez. Growing model for the supply network in a tourism destination

  13. Laura Hernandez, Stéphanie Saba and Annick Vignes. Ecological study of Auction and Decentralized Markets. Looking for the social aspect of economic exchanges

  14. Szabolcs Horvát.Comparing rodent and primate cortical connectomes based on a universal cortical organization principle

  15. Flavio Iannelli, Andreas Koher and Igor Sokolov. Hidden Pattern Geometries for Epidemics Spreading on Complex Networks

  16. Pablo Jensen, Sébastian Grauwin, Kris Lund and Heisawn Jeong. Learning about Educational Sciences through network analysis

  17. Guy Kelman, Eran Manes, Marco Lamieri and David Bree. Missing Data as Part of the Social Behavior in Networks

  18. Yongjun Liao, Wei Du, Márton Karsai and Eric Fleury.Prepaid or Postpaid? That is the question

  19. Francesca Lipari, Alberto Antonioni and Massimo Stella. “Keeping up with the Joneses”: Investigating peer and sorting effects within an adaptive network model

  20. Xiao Liu, Hai Zhao, Ying Feng, Xuan He, Jin-fa Wang and Rus Hoelzel. Pine-caterpillar networks in Changbai Mountain based on the space and time influence domain

  21. Daniela Lopez, Patricio Camus, Nelson Valdivia and Sergio Estay. Dynamics of the degree distribution in ecological networks

  22. David Martínez and Eduardo Mojica. Entropy Measures in Evolving Networks

  23. Andrea Martini, Alessio Cardillo and Paolo De Los Rios. Automatic identification of relevant concepts in scientific publications

  24. Cornelia Metzig. The Needle Sharing Network of Injecting Drug Users and Transmission of Hepatitis C

  25. Gabriel Michau, Patrice Abry, Pierre Borgnat, Nelly Pustelnik, Alfredo Nantes and Edward Chung. Estimation of Link-Dependent Origin-Destination Matrix for Traffic on Road Networks

  26. Mizuki Oka, Koya Sato, Yasuhiro Hashimoto and Takashi Ikegami. Emergence of Individuality on Social Tagging Dynamics

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