9:00 AM - 10:00 AM Keynote by Prof. Jure Leskovec, Assistant Professor, Stanford University
Abstract: Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuro, and social sciences. Present network algorithms are almost exclusively focusing on first-order, or edge-based, structures in networks. However, what is missing from the picture are methods for analyzing higher-order organization of complex networks. We present a generalized framework for a network clustering and classification based on higher-order network connectivity patterns. This framework allows for identifying rich higher-order clusters in networks. Our framework scales to networks with billions of edges and provides mathematical guarantees on the optimality of obtained clusters. We apply our framework to networks from a variety of scientific domains with scales ranging from a few hundred to over one billion links.Speaker Bio: Dr. Jure Leskovec is an Assistant Professor of Computer Science at Stanford University. His research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. He investigates problems that are motivated by large scale data, the Web and on-line media. He is also a Chief Scientist at Pinterest, where he is focusing on machine learning problems.
2:00 PM - 3:00 PM Keynote by Prof. Laks V.S. Lakshmanan, Professor, University of British ColumbiaTitle: Viral Marketing 2.0
Abstract: Over the last decade, there has been considerable excitement and research on the study and exploitation of the spread of information and influence over networks. Tremendous advances have been made on the prototypical problem of selecting a small number of seed users to activate over a social network such that the number of activated nodes in an expected sense is maximized, under several standard information diffusion models. Scalable heuristics, but more notably scalable approximation algorithms, have been developed in the recent years. Unfortunately, the state of the art has several shortcomings.
Firstly, most of
the research has focused on a simplistic setting where one marketing
campaign is active at a
time. While there has been some work on modeling and optimizing for competing
diffusions, the key role played by the network owner in a campaign has
Secondly, the relationship and contract needed between the network owner and the
advertisers is not captured. Thirdly, in real life, relationships between
multiple campaigns may be
more complex than just pure competition. Finally, most of the studies assume that the
seeds must be chosen all at once before the campaign starts with no opportunity to
observe the performance of seeds chosen earlier and course-correct as needed. We make a
call to arms for opening up the framework of viral marketing to allow for more
expressive business models and seed selection strategies, and present
recent research from our
group that addresses the modeling and computational challenges.