Albert-László Barabási is both the Robert Gray Dodge Professor of Network Science and a Distinguished University Professor at Northeastern University, where he directs the Center for Complex Network Research, and holds appointments in the Departments of Physics and Computer Science, as well as in the Department of Medicine, Harvard Medical School and Brigham and Women Hospital, and is a member of the Center for Cancer Systems Biology at Dana Farber Cancer Institute. A Hungarian born native of Transylvania, Romania, he received his Masters in Theoretical Physics at the E√∂tv√∂s University in Budapest, Hungary and was awarded a Ph.D. three years later at Boston University. Barabási latest book is "Bursts: The Hidden Pattern Behind Everything We Do" (Dutton, 2010) available in five languages. He has also authored "Linked: The New Science of Networks" (Perseus, 2002), currently available in eleven languages, and is the co-editor of "The Structure and Dynamics of Networks" (Princeton, 2005). His work lead to the discovery of scale-free networks in 1999, and proposed the Barabási-Albert model to explain their widespread emergence in natural, technological and social systems, from the cellular telephone to the WWW or online communities.

Barabási is a Fellow of the American Physical Society. In 2005 he was awarded the FEBS Anniversary Prize for Systems Biology and in 2006 the John von Neumann Medal by the John von Neumann Computer Society from Hungary, for outstanding achievements in computer-related science and technology. In 2004 he was elected into the Hungarian Academy of Sciences and in 2007 into the Academia Europaea. He received the C&C Prize from the NEC C&C Foundation in 2008. In 2009 APS chose him Outstanding Referee and the US National Academies of Sciences awarded him the 2009 Cozzarelli Prize. In 2011 Barabási was awarded the Lagrange Prize-CRT Foundation for his contributions to complex systems, awarded Doctor Honoris Causa from Universidad Politécnica de Madrid, became an elected Fellow in AAAS (Physics) and is an 2013 Fellow of the Massachusetts Academy of Sciences.

Northeastern University
*Robert Gray Dodge Professor of Network Science
*Distinguished University Professor
*Director, Center for Complex Network Research

Harvard University
*Lecturer in Medicine, Department of Medicine at both the Dana-Farber Cancer Institute
*Division of Network Medicine, Brigham and Women's Hospital

Central European University
*Visiting Professor, Center for Network Science

Resilience, a system’s ability to adjust its activity to retain its basic functionality under errors, failures and environmental changes, is a defining property of many complex systems. Despite widespread consequences on human health, economy and the environment, events leading to loss of resilience, from cascading failures in technological systems to mass extinctions in ecological networks, are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience uses low dimensional models of a few interacting components to characterize multi-dimensional systems consisting of a large number of components that interact through a complex network. Here I discuss results that bridge this theoretical gap. To do we developed a set of analytical tools to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive an effective one dimensional dynamics that accurately predicts the system’s resilience. The proposed analytical framework allows us to systematically separate the role of the system’s dynamics and topology, collapsing the behavior of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering avenues to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems that are resilient to external perturbations and environmental changes alike.
  • Jeong Han Kim

    Jeong Han Kim
    Korea Institute for Advanced Study
    How to Generate Random Graphs?
    Biography & Abstract
In this talk, we will present an algorithm to generate a random graph that is essentially the same as the Erdős–Rényi random graph. The algorithm, called the cut-off line algorithm (COLA), enables us to generate the random graph and simultaneously analyze certain problems. As a consequence, the size of the largest component of the Erdős–Rényi random graph can be described very precisely. We hope that the algorithm can be modified to generate other types of random graphs such as scale-free random graphs.
  • Janos Kertesz

    Janos Kertesz
    Central European University (CEU)
    Social Contagion: A Truly Complex Phenomenon
    Biography & Abstract

Janos Kertesz obtained his PhD in Physics from Eotvos University, Budapest. He has been professor since 1992 at the Budapest University of Technology and Economics, and since 2012 at the Center for Network Science of the Central European University, where he is director of the first Network Science PhD Program in Europe. He was visiting scientist in Germany, US, France, Italy and Finland. He is interested in statistical physics and its applications, including percolation theory, phase transitions, fractal growth, granular materials and simulation methods. His research focuses on multidisciplinary topics, mainly on complex networks and on financial analysis and modeling. He has been on the editorial boards of Journal of Physics A, Physica A, Fluctuation and Noise Letters, Fractals, New Journal of Physics and Phys. Rev. E. Janos Kertesz has published more than 250 papers and his work has been awarded by several recognitions, including the title of Finland Distinguished Professor and the Szechenyi Prize of the Hungarian State. Since 2001 he has been elected member of the Hungarian Academy of Sciences.

Central European University
Center for Network Science

Budapest University of Technology and Economics
Department of Theoretical Physics

Spreading of information, behavioral patterns or innovations has substantial impact on our lives. Analogies with disease spread was discovered already in the sixties but important differences, especially multiple interactions and the existence of endogenous influence, e.g., through the media make this type of contagion complex. The main influencing factors the speed of spreading include the topology of the aggregateof the underlying temporal network, the inhomogeneity of activities and its correlation with the network topology and the often bursty pattern of interactions, as well as the sensitivity to peer pressure. Large scale data enable to understand the role of these contributions and to construct a theoretical framework to describe them.

After receiving an undergraduate degree in biochemistry, Olaf Sporns earned a PhD in neuroscience at Rockefeller University and then conducted postdoctoral work at The Neurosciences Institute in New York and San Diego. Currently he is the Robert H. Shaffer Chair, a Distinguished Professor, and a Provost Professor in the Department of Psychological and Brain Sciences at Indiana University in Bloomington. He is co-director of the Indiana University Network Science Institute and holds adjunct appointments in the School of Informatics and Computing and the School of Medicine. His main research area is theoretical and computational neuroscience, with a focus on complex brain networks. He has authored over 190 peer-reviewed publications as well as the recent books “Networks of the Brain” and “Discovering the Human Connectome”, published by MIT Press. Sporns was awarded a John Simon Guggenheim Memorial Fellowship in 2011 and was elected Fellow of the American Association for the Advancement of Science in 2013.

Modern neuroscience is in the middle of a transformation, driven by the development of novel high-resolution brain mapping and recording technologies that deliver increasingly large and detailed “big neuroscience data”. Network science has emerged as one of the principal approaches to model and analyze neural systems, from individual neurons to circuits and systems spanning the whole brain [1,2]. A core theme of network neuroscience is the comprehensive mapping of anatomical and functional brain connectivity, also called connectomics. In this lecture I will review current themes and future directions of network neuroscience, including comparative studies of brain networks across different animal species, investigation of prominent network attributes in human brains, and use of computational models to map information flow and communication dynamics. I will argue that network neuroscience represents a promising theoretical framework for understanding the complex structure, operations and functioning of nervous systems.

[1] Sporns, O. (2011) Networks of the Brain. MIT press.
[2] Sporns, O. (2014) Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience 17, 652-660.