Computational Concerns in Statistical Inference and Learning for Network Data Analysis

Job Candidate Talk
Thursday, January 12, 2017 - 11:05am for 1 hour (actually 50 minutes)
Skiles 006
Tengyuan Liang – University of Pennsylvania – tengyuan@wharton.upenn.edu
Michael Damron
Network data analysis has wide applications in computational social science, computational biology, online social media, and data visualization. For many of these network inference questions, the brute-force (yet statistically optimal) methods involve combinatorial optimization, which is computationally prohibitive when faced with large scale networks. Therefore, it is important to understand the effect on statistical inference when focusing on computationally tractable methods. In this talk, we will discuss three closely related statistical models for different network inference problems. These models answer inference questions on cliques, communities, and ties, respectively. For each particular model, we will describe the statistical model, propose new computationally efficient algorithms, and study the theoretical properties and numerical performance of the algorithms. Further, we will quantify the computational optimality through describing the intrinsic barrier for certain efficient algorithm classes, and investigate the computational-to-statistical gap theoretically. A key feature shared by our studies is that, as the parameters of the model changes, the problems exhibit different phases of computational difficulty.