AAlto University, Finland
Oxford University, UK
Prof Heather Harrington is a Royal Society University Research Fellow and Associate Professor in the Mathematical Institute at the University of Oxford. She is Co-Director of the Centre for Topological Data Analysis. Her research focuses on the problem of reconciling models and data by extracting information about the structure of models and the shape of data. To develop these methods, Prof Harrington integrates techniques from a variety of disciplines such as computational algebraic geometry and topology, statistics, optimisation, network theory, linear algebra, and dynamical systems. Based on this research, she was recently awarded a London Mathematical Society Whitehead Prize.
Persistent homology (PH) is a technique in topological data analysis that allows one to examine features in data across multiple scales in a robust and mathematically principled manner, and it is being applied to an increasingly diverse set of applications. We investigate applications of PH to dynamic biological networks with concrete examples from contagions, neuroscience, and blood vessels.
Technical University of Denmark, Denmark
Sune Lehmann is an associate professor at the Technical University of Denmark, an adjunct (full) professor at University of Copenhagen's Department of Sociology, and an adjunct associate professor at the Niels Bohr Institute (Department of Physics, University of Copenhagen). Sune is the associate director of the Center for Social Data Science at University of Copenhagen. Sune's work focuses on the dynamics of complex networks as well as processes unfolding on such evolving networks. He is the author of multiple highly cited papers and his work has received world-wide press coverage.
In other to understand the multi-layered and dynamic social interactions within a large social system, I equipped 1000 freshmen students at the Technical University of Denmark with top-of-the-line smartphones running custom software designed to collect interactions mediated through face-to-face meetings (proximity estimated via Bluetooth), telecommunication (phone-calls, text messages), and online social networks (Facebook friendships and interactions). The phones also collected geo-locations, wifi-signals, and a number of other data channels; participants also answered paneled questionnaires regarding personality, study habits, and health-related behavior. The data collection lasted 2.5 years. Through this rich dataset, we have learned about much more than social networks. In my talk, I will discuss key findings from this study, with an emphasis on communities in dynamic networks and recent results on human mobility.
City College of New York, USA
Hernan research focuses on the theoretical understanding of Complex Systems from a Statistical Physics viewpoint. He is working towards the development of new emergent laws for complex systems, ranging from brain networks to biological networks and social systems. Treating these complex systems from a unified theoretical approach, he uses
concepts from statistical mechanics, network and optimization theory, machine learning, and big-data science to advance new views on complex systems and networks.
Identifying essential nodes in complex networks is a central problem for biological systems to social systems. We treat this problem in three paradigmatic cases: the brain, ecosystems and social networks. Mathematically, we find the set of influential nodes by optimizing the damage to the giant connected component with systematic inactivation of nodes. We then apply network theory and pharmacogenetic interventions in a rat brain to predict and target essential nodes responsible for global integration in a model of learning and memory. We find that the integration of the brain network is mediated by a set of weak nodes through optimization of influence in optimal percolation. Pharmacogenetic inhibitions confirm the theoretical predictions. We discuss the relevance of these influencers to ecological systems dominated by abrupt first order tipping points as well as connectomes with regularities.
Universitat Politècnica de Catalunya, Spain
RWTH Aachen University, Germany
UMass Amherst, USA
Professor Towsley's research spans a wide range of activities from stochastic analyses of queueing models of computer and telecommunications to the design and conduct of measurement studies. He has performed some of the pioneering work on the exact and approximate analyses of parallel/distributed applications and architectures. More recently, he pioneered the area of network tomography and the use of fluid models for large networks. He has published extensively, with over 150 articles in leading journals. PhD Computer Science, University of Texas (1975), BA Physics, University of Texas (1971). Professor Towsley first joined the faculty at the University of Massachusetts in the Department of Electrical and Computer Engineering in 1976 and moved to the College of Information and Computer Sciences in 1986. He was named University Distinguished Professor of Computer Science in 1998. Professsor Towsley was a Visiting Scientist at the IBM T.J. Watson Research Center, (1982-83, 2003), INRIA and AT&T Labs - Research (1996-97), and Cambridge Microsoft Research Lab (2004); a Visiting Professor at the Laboratoire MASI, Paris, (1989-90). Professor Towsley has been an editor of the IEEE Transactions on Communications, IEEE/ACM Transactions on Networking, and Journal of Dynamic Discrete Event Systems. He is currently on the Editorial boards of Networks and Performance Evaluation. He was a Program Co-chair of the joint ACM SIGMETRICS and PERFORMANCE '92 conference. He is a two-time recipient of the Best Paper Award of the ACM Sigmetrics Conference. He is a Fellow of the IEEE and of the ACM. He is also a member of ORSA and is active in the IFIP Working Groups 6.3 on Performance Modeling of Networks and 7.3 on Performance Modeling. Towsley is the recipient of one of the IEEE's most prestigious honors, the 2007 IEEE Koji Kobayashi Computers and Communications Award. He also received a UMass Amherst Distinguished Faculty Lecturer award in 2002 and a UMass Amherst College of Natural Sciences and Mathematics Faculty Research Award in 2003.
Complex networks that occur in nature, such as those from biochemistry, neurobiology, and engineering, often exhibit simple, network structural properties, or “motifs.” Network motifs refer to recurring, significant patterns of interaction between sets of nodes and represent basic building blocks of graphs. Motifs in social networks exhibit spatial patterns and temporal patterns that vary according to the type of network. This talk reports on these variations across several network types and identify several common substructures. Reciprocity of directed ties occurs much more frequently than expected by chance in all networks. Similarly, we find that completely connected triads and tetrads (i.e., four-node sub-graphs) occur more often than expected, highlighting the tendency of actors to form clusters of ties. We also identify motifs that suggest patterns of hierarchy. Motifs are also useful for the purpose of sub-graph classification. We demonstrate their value in identifying the type of network that a sub-graph belongs to.
We also consider the challenge of characterizing motifs in large graphs, and show how carefully designed sampling algorithms can accurately characterize them using a small number of samples.
Last, we close with open problems regarding motifs whose solution can lead to better understanding of social networks and analytical tools for characterizing them.