Recent advances in computing and measurement technologies have led to an explosion in the amount of data that are being collected in all areas of application. Much of these data have complex structure, in the form of text, images, video, audio, streaming data, and so on. The proposed session focuses on one important class of problems, viz, data with network structures. Such data are common in diverse engineering and scientific areas, such as biology, computer science, economics, business, epidemiology, sociology and so on. As a consequence, research on networks has steadily increased in recent years, and has also appeared in leading science publications. For example, Nature has published several reviews on the subject, Science and PNAS devoted special issues to it. While there has been extensive research on networks and practical successes, much of it happen outside the field of Statistics, and our theoretical and methodological understanding of their statistical properties is still limited. This offers statisticians numerous open questions and opportunities to be involved and allows statisticians to play critical roles. The scope of the invited talks in the proposed session ranges from characterizing and modeling network structures based on statistical principles, to exploiting the network structure as additional information to develop statistical learning methods, as well as applications to understand political partisan polarization in social media interactions among both political elites and the public. The four speakers and one discussant (including three males and two females) in the proposed session are a mixture of outstanding early- and mid- career statisticians, who come from different geographic locations (three are from the U.S., one from Canada, and one from Europe) and have extensive experience in computing, theory, methodology and applications. It is my belief that in order for the statistics community to grow healthily, it is vital to provide opportunities for the early- and mid- career statisticians to disseminate their research findings. The four invited speakers will present their most recent progresses in the area of statistical network analysis and provide new directions from the statistical perspective. The discussant will provide professional and constructive criticisms and raise issues for broader consideration as a way to connect the invited talks. Their unique insights will be valuable to the broad scientific community working on cutting-edge network problems.

Speaker: Eric Kolaczyk
Title: Coevolving Latent Space Network with Attractors Models for Polarization
Abstract: We develop a broadly applicable class of coevolving latent space network with attractors (CLSNA) models, where nodes represent individual social actors assumed to lie in an unknown latent space, edges represent the presence of a specified interaction between actors, and attractors are added in the latent level to capture the notion of attractive and repulsive forces. We apply the CLSNA models to understand the dynamics of partisan polarization on social media, where we expect US Republicans and Democrats to increasingly interact with their own party and disengage with the opposing party. Our analysis confirms the existence of partisan polarization in social media interactions among both political elites and the public. Moreover, while attractive partisanship is the driving force of interactions across the full periods of study for both the public and Democratic elites, repulsive partisanship has come to dominate Republican elites' interactions since the run-up to the 2016 presidential election.

Speaker: Maggie Niu
Title: Learning Network Properties without Network Data -- A Correlated Network Scale-up Model
Abstract: The network scale-up method based on "how many X's do you know?" questions has gained popularity in estimating the sizes of hard-to-reach populations. The success of the method relies primarily on the easy nature of the data collection and the flexibility of the procedure, especially since the model does not require a sample from the target population, a major limitation of traditional size estimation models. In this talk, we propose a new network scale-up model which incorporates respondent and subpopulation covariates in a regression framework, includes a bias term that more accurately estimates response biases under fewer assumptions, and adds a correlation structure between subpopulations. In addition to estimating the unknown population sizes, our proposed model depicts people's social network patterns in an aggregated level without using the network data.

Speaker: Gongjun Xu
Title: High-dimensional Factor Analysis for Network-linked Data
Abstract: Factor analysis is a widely used statistical tool in many scientific fields, including psychology, economics, and sociology. Meanwhile, observations connected by a network are becoming increasingly common, and how to incorporate the network structure in factor analysis remains an open question. Focusing on high-dimensional factor analysis with network-connected observations, we propose a generalized factor model with latent factors capturing both the network structure and the dependence structure among the high-dimensional variables. We develop a computationally efficient estimation procedure and establish asymptotic inferential theories. In particular, we show that by borrowing information from the network, the proposed estimator of the factor loading matrix achieves the optimal asymptotic variance under much milder identifiability constraints than the existing literature.

Speaker: Yi Yu
Title: Change Point Localisation in Dynamic Multilayer Random Dot Product Graphs
Abstract: In this talk we are concerned with change point localisation in a dynamic multilayer network, which is modelled through a sequence of random dot product graphs in tensor forms, with temporal dependence and cross-layer dependence. We will demonstrate the minimax optimality of our proposed change point estimators and support the theoretical findings through extensive numerical experiments.

Organiser: Prof. Ji Zhu 

Chair: Prof. Ji Zhu 

Speaker: Prof. Eric Kolaczyk 

Speaker: Dr Gongjun Xu 

Speaker: Maggie Niu 

Speaker: Yi Yu  

Discussant:  Tianxi Li