Learn methods for understanding complex networks by examining the processes within networks spanning biological, traffic and the brain in this talk with Ambuj K. Singh, professor of computer science at the University of California, Santa Barbara, hosted by Assistant Professor Hanghang Tong.
Detecting Significant Network Processes
Presented by Ambuj Singh, professor of computer science at the University of California, Santa Monica.
Friday, May 25, 2018
Brickyard (BYENG) 209, Tempe campus [map]
In order to understand complex networks, we need to characterize the dynamic processes occurring within them. Examples of such processes include network congestion on the internet or a transportation network, or the spread of malware through an online social network. The detection of such dynamic processes requires a model of underlying behavior using which inferences about significance or anomalous behavior can be made. Detecting anomalies in networks is a well-understood problem when restricted to only the graph structure (e.g., communities, structural holes), but there has been limited work on networks with node/edge attributes. When node attributes are allowed to change over time, the smooth evolution of network substructures can be used to detect significant network processes that grow, shrink and merge over time. Ambuj K. Singh will discuss an approach that compares the value at a node/edge with a background distribution, and uses a positive score to indicate significance. He will discuss methods for detecting highest scoring subgraphs (fixed substructures, varying time intervals), and for detecting smoothly varying substructures. Finally, Singh will discuss methods that detect significant network substructures over two classes of networks in order to explain their differences. Examples will be drawn from biological networks, traffic networks and brain networks to illustrate the methods.
About the speaker
Ambuj K. Singh is a professor of computer science at the University of California, Santa Barbara, with part-time appointments in the Bimolecular Science and Engineering Program and the Technology Management Program. He received a B.Tech. degree from the Indian Institute of Technology, Kharagpur, and a doctoral degree from the University of Texas at Austin. His research interests are broadly in the areas of network science, machine learning, and bioinformatics. He has led a number of multidisciplinary projects. He is currently directing UCSB’s Interdisciplinary Graduate Education Research and Training (IGERT) program on Network Science funded by the National Science Foundation (NSF), and the Multidisciplinary University Research Initiative (MURI) on Network Science of Teams. He has graduated over 20 doctoral students over his career.