Donald Towsley

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.


Motifs in Social Networks

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.