Abstract Number:
1609
Submission Type:
Topic-Contributed Paper Session
Participants:
Emma Jingfei Zhang (1), Chang Su (1), Yuguo Chen (2), Subhadeep Paul (3), Srijan Sengupta (4), Daniel Sewell (5), Emma Jingfei Zhang (1)
Institutions:
(1) Emory University, N/A, (2) University of Illinois at Urbana-Champaign, N/A, (3) The Ohio State University, N/A, (4) North Carolina State University, N/A, (5) University of Iowa, N/A
Chair:
Session Organizer:
Speaker(s):
Yuguo Chen
University of Illinois at Urbana-Champaign
Session Description:
In this new era of data science, network data are fast emerging, and they are of great interest in a large variety of scientific disciplines. Recent advances in data collection technology, such as automatic monitoring devices in social studies or diffusion tensor imaging in neuroimaging, have multiplied the availability of network data, leading to not only larger and but also more complex networks. While the network data analysis literature has grown rapidly in the past decade, we are constantly confronted with new statistical challenges. This session aims to share recent advances in the statistical analysis of complex network data, feature under-studied problems of critical importance, shape future directions in the field and create a platform for interdisciplinary collaboration.
This topic contributed session features four mid-career and established scholars at the forefront the field to share their most recent work on network data analysis. Their research addresses new challenges in the field of network analysis including the analysis of dynamic networks, clique detection, edge clustering, statistical inference for network models, and represents the next frontier of this field. The section will appeal to both methodological, theoretical, and applied statisticians and practitioners.
Below are the tentative titles and short abstracts (SA) from the confirmed speakers (alphabetically ordered by last name).
1. Yuguo Chen - Inferring Social Influence in Dynamic Networks
SA: We build a longitudinal influence model to specify how people's behaviors are influenced by others in a dynamic network. In order to determine the degrees of influence, we propose a sequential hypothesis testing procedure and use generalized estimating equations to account for multiple observations of the same individual across different time points.
2. Subhadeep Paul - Joint latent space model for social networks with multivariate attributes
SA: We propose a joint latent space model (JLSM) that summarizes information from the social network and the multivariate attributes in a person-attribute joint latent space. We develop a variational Bayesian expectation–maximization estimation algorithm to estimate the attribute and person locations in the joint latent space.
3. Srijan Sengupta - Anomalous clique detection and identification in inhomogeneous networks
SA: We consider the following problem: given a large background network, we want to know whether the network contains an anomalous clique (the detection problem). If the answer to this question is yes, then we further want to identify the members of this anomalous clique (the identification problem).
4. Daniel Sewell - Automated detection of edge clusters
SA: We propose a model-based approach to clustering the edges of a network using a latent space model describing the features of both actors and latent environments. Within a Bayesian framework, we use a sparse mixture prior that supports automated selection of the number of clusters.
5. Emma Jingfei Zhang - Decoupling in Network Community Detection
SA: We describe a new approach based on decoupling in the likelihood function, which enables a fast alternating maximization algorithm with guaranteed convergence. We apply this approach to the stochastic blockmodel and some of its variants and demonstrate the effectiveness of the proposed method.
Sponsors:
IMS 3
Section on Statistical Computing 1
Section on Statistical Graphics 2
Theme:
Statistics and Data Science: Informing Policy and Countering Misinformation
Yes
Applied
No
Estimated Audience Size
Small (<80)
I have read and understand that JSM participants must abide by the Participant Guidelines.
Yes
I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.
I understand