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Spotlight
Dynamic Network Model from Partial Observations
Elahe Ghalebi · Baharan Mirzasoleiman · Radu Grosu · Jure Leskovec

Wed Dec 05 12:30 PM -- 12:35 PM (PST) @ Room 220 E
in Wed Track 2 -- Session 2 »

Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic cascading processes (e.g., information diffusion, virus propagation) occurring over the unobserved network. While there have been efforts to infer networks based on such data, providing a generative probabilistic model that is able to identify the underlying time-varying network remains an open question. Here we consider the problem of inferring generative dynamic network models based on network cascade diffusion data. We propose a novel framework for providing a non-parametric dynamic network model---based on a mixture of coupled hierarchical Dirichlet processes---based on data capturing cascade node infection times. Our approach allows us to infer the evolving community structure in networks and to obtain an explicit predictive distribution over the edges of the underlying network---including those that were not involved in transmission of any cascade, or are likely to appear in the future. We show the effectiveness of our approach using extensive experiments on synthetic as well as real-world networks.

Author Information

Elahe Ghalebi (TU Wien)
Baharan Mirzasoleiman (Stanford University)
Radu Grosu (TU Wien)

Radu Grosu is a full Professor, and the Head of the Institute of Computer Engineering, at the Faculty of Informatics, of the Vienna University of Technology. Grosu is also the Head of the Cyber-Physical-Systems Group within the Institute of Computer-Engineering, and a Research Professor at the Department of Computer Science, of the State University of New York at Stony Brook, USA. The research interests of Radu Grosu include the modeling, the analysis and the control of cyber-physical systems and of biological systems. The applications focus of Radu Grosu includes distributed automotive and avionic systems, autonomous mobility, green operating systems, mobile ad-hoc networks, cardiac-cell networks, and genetic regulatory networks. Radu Grosu is the recipient of the National Science Foundation Career Award, the State University of New York Research Foundation Promising Inventor Award, the Association for Computing Machinery Service Award, and is an elected member of the International Federation for Information Processing, Working Group 2.2. Before receiving his appointment at the Vienna University of Technology, Radu Grosu was an Associate Professor in the Department of Computer Science, of the State University of New York at Stony Brook, where he co- directed the Concurrent-Systems Laboratory and co-founded the Systems-Biology Laboratory. Radu Grosu earned his doctorate (Dr.rer.nat.) in Computer Science from the Faculty of Informatics of the Technical University München, Germany. He was subsequently a Research Associate in the Department of Computer and Information Science, of the University of Pennsylvania, USA, and an Assistant Professor in the Department of Computer Science, of the State University of New York at Stony Brook, USA.

Jure Leskovec (Stanford University and Pinterest)

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