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1. Causal Network Analysis

   https://doi.org/10.1146/annurev-soc-030320-102100  

   An, Weihua; Beauvile, Roberson; Rosche, Benjamin (July 2022, Annual Review of Sociology)

   Fueled by recent advances in statistical modeling and the rapid growth of network data, social network analysis has become increasingly popular in sociology and related disciplines. However, a significant amount of work in the field has been descriptive and correlational, which prevents the findings from being more rigorously translated into practices and policies. This article provides a review of the popular models and methods for causal network analysis, with a focus on causal inference threats (such as measurement error, missing data, network endogeneity, contextual confounding, simultaneity, and collinearity) and potential solutions (such as instrumental variables, specialized experiments, and leveraging longitudinal data). It covers major models and methods for both network formation and network effects and for both sociocentric networks and egocentric networks. Lastly, this review also discusses future directions for causal network analysis. Expected final online publication date for the Annual Review of Sociology, Volume 48 is July 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates. 

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2. Differential Network Analysis

   Zhang, Peng; Gember-Jacobson, Aaron; Zuo, Yueshangl; Liu, Xu; Li, Hao (January 2022, 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI))
3. Ordered network analysis

   https://doi.org/10.1007/978-3-031-31726-2_8  

   Tan, Y.; Ruis, A.R.; Marquart, C.L.; Cai, Z.; Knowles, M.; Shaffer, D.W. (January 2023, Advances in Quantitative Ethnography: 4th International Conference, ICQE 2022)
4. Epistemic Network Analysis Visualization

   https://doi.org/10.1007/978-3-030-93859-8_9  

   Tan, Y. (January 2022, Communications in computer and information science)

   Wasson, B. (Ed.)

   Visualization plays an important role in Epistemic Network Analysis (ENA), not only in graphical representation but also to facilitate interpretation and communicate research findings. However, there is no published description of the design features behind ENA network graphs. This paper provides this description from a graphic design perspective, focusing on the design principles that make ENA network graphs aesthetically pleasing and intuitive to understand. By reviewing graphic design principles and examining other extant network visualizations, we show how the current ENA network graphs highlight the most important network characteristics and facilitate sense-making. 

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5. Directed Epistemic Network Analysis

   https://doi.org/https://doi.org/10.1007/978-3-030-67788-6_9  

   Fogel, Ariel; Swiecki, Zachari; Marquart, Cody; Cai, Zhiqiang; Brohinsky, Jais; Siebert-Evenston, Amanda; Eagan, Brendan; Ruis, Andrew R.; Shaffer, David W. (January 2021, Advances in Quantitative Ethnography: Second International Conference, ICQE 2020, Malibu, CA, USA, February 1-3, 2021, Proceedings)

   Ruis, Andrew R.; Lee, Seung B. (Ed.)

   Quantitative ethnographers across a range of domains study complex collaborative thinking (CCT): the processes by which members of a group or team develop shared understanding by making cognitive connections from the statements and actions of the group. CCT is difficult to model because the actions of group members are interdependent—the activity of any individual is influenced by the actions of other members of the group. Moreover, the actions of group members engaged in some collaborative tasks may need to follow a particular order. However, current techniques can account for either interdependence or order, but not both. In this paper, we present directed epistemic network analysis (dENA), an extension of epistemic network analysis (ENA), as a method that can simultaneously account for the interdependent and ordered aspects of CCT. To illustrate the method, we compare a qualitative analysis of two U.S. Navy commanders working in a simulation to ENA and dENA analyses of their performance. We find that by accounting for interdependence but not order, ENA was not able to model differences between the commanders seen in the qualitative analysis, but by accounting for both interdependence and order, dENA was able to do so. 

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