Zack W. Almquist

Zack W. Almquist

Professor of Sociology, Adjunct Professor of Statistics and Senior Data Science Fellow @ eScience

Social Network Methods

My research in Social Networks/Network Science methods builds on concepts and techniques developed for studying social networks. Networks offer a powerful and compelling framework for understanding fundamental relations, whether those driving an individual’s life and success (e.g., advice, friendship) or those governing large organizations that influence our society (e.g., communications between FEMA and local governments). While these tools are valuable in their unique combination of generalizability and precision in measuring our social world, too often, they are limited in the contextual information they include. For example, network researchers have historically modeled network data using only formal features of the network, such as the size of an individual’s friend group, while ignoring other important characteristics such as how often the friends interact (time), and how close they live to one another (space). This limited use of contextual information is a major failing of much of the social network literature, which overlooks our environment’s enormous impact on our behavior. By addressing contextual mechanisms through the powerful social network lens, we can improve our understanding of social processes and our ability to predict social action — this is the central theme of my research agenda.

Spatial Network Models

Peer Reviewed Articles

  • Thomas, L. J., P. Huang, F. Yin, J. Xu, Z. W. Almquist, J. R. Hipp, and C. T. Butts (2022). Geographical Patterns of Social Cohesion Drive Disparities in Early COVID Infection Hazard. Proceedings of the National Academy of Sciences 119(22).
  • Abel, G. J., J. DeWaard, J. Trang Ha, and Z. W. Almquist (2021). The Form and Evolution of International Migration Networks, 1960-2015. Population, Space and Place 27(3), 1–15.
  • Thomas, L. J., P. Huang, F. Yin, X. I. Luo, Z. W. Almquist, J. R. Hipp, and C. T. Butts (2020). Spatial Heterogeneity Can Lead to Substantial Local Variations in COVID-19 Timing and Severity. Proceedings of the National Academy of Sciences 117(39), 24180–24187.
  • Almquist, Z. W. (2020). Large-scale Spatial Network Models: An application to modeling information diffusion through the homeless population of San Francisco. Environment and Planning B: Urban Analytics and City Science 47(3), 523–540.
  • Almquist, Z. W. and B. E. Bagozzi (2016). The Spatial Properties of Radical Environmental Organizations in the UK: Do or Die! PloS ONE 11(11), 1–19.
  • Spiro, E. S., Z. W. Almquist, and C. T. Butts (2016). The Persistence of Division: Geography, Institutions, and Online Friendship Ties. Socius: Sociological Research for a Dynamic World 2(1), 1–15.
  • Almquist, Z. W. and C. T. Butts (2015). Predicting Regional Self-Identification from Spatial Network Models. Geographical Analysis 47(1), 50–72.
  • Smith, E. J., C. S. Marcum, A. Boessen, Z. W. Almquist, J. R. Hipp, N. N. Nagle, and C. T. Butts (2014). The Relationship of Age to Personal Network Size, Relational Multiplexity, and Proximity to Alters in the Western United States. The Journal of Gerontology: Series B 70(1), 91–99.
  • Boessen, A., J. R. Hipp, E. J. Smith, C. T. Butts, N. N. Nagle, and Z. W. Almquist (2014). Networks, Space, and Residents’ Perception of Cohesion. American Journal of Community Psychology 53(3), 447–461.
  • Almquist, Z. W. and C. T. Butts (2012). Point process models for household distributions within small areal units. Demographic Research 26(22), 593–632.

Dynamic Network Models

Peer Reviewed Articles and Working Papers

  • Mallik, A. and Z. W. Almquist (2019). Stable Multiple Time Step Simulation/Prediction from Lagged Dynamic Network Regression Models. Journal of Computational and Graphical Statistics 28(4), 967–979.
  • Almquist, Z. W. and C. T. Butts (2018). Dynamic Network Analysis with Missing Data: Theory and Methods. Statistica Sinica 28(3), 1245–1264.
  • Almquist, Z. W., E. S. Spiro, and C. T. Butts (2016). “Shifting Attention: Modeling Follower Relationship Dynamics among US Emergency Management-related Organizations During a Colorado Wildfire.” In: Social Network Analysis of Disaster Response, Recovery, and Adaptation. Ed. by A. Faas and E. Jones. Philadelphia, PA: Elsevier.
  • Butts, C. T. and Z. W. Almquist (2015). A Flexible Parameterization for Baseline Mean Degree in Multiple-Network ERGMs. The Journal of Mathematical Sociology 39(3), 163–167.
  • Almquist, Z. W. and C. T. Butts (2014). “Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics”. In: Bayesian Inference in the Social Sciences. Ed. by I. Jeliazkov and X.-S. Yang. Hoboken, New Jersey: John Wiley & Sons.
  • Almquist, Z. W. and C. T. Butts (2014). Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics. Sociological Methodology 44(1), 273–321.
  • Almquist, Z. W. and C. T. Butts (2013). Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-group Blog Citation Dynamics in the 2004 US Presidential Election. Political Analysis 21(4), 430–448.
  • Valluvan, R., Z. W. Almquist, A. Anandkumar, and C. T. Butts (2012). Modeling Dynamic Social Networks with Vertex Evolution via Latent Graphical Models. Poster presentation at the 2012 NIPS Workshop on Social Network and Social Media Analysis, Lake Tahoe, NV.

Personal Networks/Egocentric Networks/Network Sampling

Peer Reviewed Articles and Working Papers

  • Almquist, Z., I. Kahveci, O. Kajfasz, J. Rothfolk, and A. Hagopian (2026). Understanding the Personal Networks of People Experiencing Homelessness in King County, WA with Aggregate Relational Data. Social Networks 86, 150–172.
  • Almquist, Z. W., I. Kahveci, A. Hazel, O. Kajfasz, J. Rothfolk, C. Guilmette, M. Anderson, L. Ozeryansky, and A. Hagopian (2025). Innovating a Community-driven Enumeration and Needs Assessment of People Experiencing Homelessness: A Network Sampling Approach for the HUD-Mandated Point-in-Time Count. American Journal of Epidemiology 194(6), 1524–1533.
  • Almquist, Z. W., A. Hazel, O. Kajfasz, J. Rothfolk, C. Guilmette, M. Anderson, L. Ozeryansky, and A. Hagopian (2023). Network Sampling Methods for Estimating Social Networks, Population Percentages, and Totals of People Experiencing Unsheltered Homelessness. arXiv preprint arXiv:2309.03875.
  • Neal, Z. P., Z. Almquist, J. Bagrow, A. Clauset, et al. (2024). Recommendations for sharing network data and materials. Network Science 12(4), 404–417.
  • Almquist, Z. W., S. Arya, L. Zeng, and E. S. Spiro (2019). Unbiased Sampling of Users from (Online) Activity Data. Field Methods 31(1), 23–38.
  • Nilakanta, H., Z. W. Almquist, and G. L. Jones (2019). Ensuring Reliable Monte Carlo Estimates of Network Properties. arXiv preprint arXiv:1911.08682.
  • Almquist, Z. W. (2012). Random errors in egocentric networks. Social Networks 34(4), 493–505.
  • Kurant, M., M. Gjoka, Y. Wang, Z. W. Almquist, C. T. Butts, and A. Markopoulou (2012). Coarse-Grained Topology Estimation via Graph Sampling. In: Proceedings of ACM SIGCOMM Workshop on Online Social Networks (WOSN) ’12. Helsinki, Finland.

Activity Networks

  • Almquist, Z. W., S. Arya, L. Zeng, and E. S. Spiro (2019). Unbiased Sampling of Users from (Online) Activity Data. Field Methods 31(1), 23–38.
  • Zeng, L., Z. W. Almquist, and E. S. Spiro (2019). “Friending” in Online Fitness Communities: Exploring Activity-Based Online Network Structure. In: Proceedings of the 52nd Hawaii International Conference on System Sciences, pp.2822–2831.
  • Zeng, L., Z. W. Almquist, and E. S. Spiro (2018). Stay Connected and Keep Motivated: Modeling Activity Level of Exercise in an Online Fitness Community. In: Social Computing and Social Media. Technologies and Analytics. Ed. by G. Meiselwitz. Vol. 10914. Lecture Notes in Computer Science. Springer International Publishing, pp.137–147.
  • Zeng, L., Z. W. Almquist, and E. S. Spiro (2017). Let’s Workout! Exploring Social Exercise in an Online Fitness Community. In: The iConference 2017 Proceedings, Wuhan, China. Vol. 2, pp.87–98.