Draft:Katherine Faust
Katherine Faust
[edit]Katherine Faust, PhD | |
|---|---|
| Born | 1954 |
| Education | Pomona College (B.A.), University of California, Irvine (M.A., Ph.D.) |
| Alma mater | Pomona College; University of California, Irvine |
| Organization(s) | University of California, Irvine |
| Title | Professor of Sociology |
Katherine Faust is an American sociologist renowned for her work in social network analysis, mathematical sociology, and quantitative methods. She is a Professor in the Department of Sociology at the University of California, Irvine (UCI) and is affiliated with the Institute for Mathematical Behavioral Sciences, the Center for Demographic and Social Analysis, and the Center for Organizational Research.[1] Faust's research combines mathematical methodologies with sociological theory to investigate network structures, focusing on comparative analysis, demographic processes, and local network patterns.[2] Her notable students include Karin Willert, Jennifer Chandler, Miruna Petrescu-Prahova, Mourad Dakhli, Gretchen Koehler.
Early Life and Education
[edit]Katherine Faust pursued her academic studies in anthropology and sociology, focusing on quantitative methods and social structures. She completed her Bachelor of Arts (B.A.) in Anthropology at Pomona College in 1976, where she graduated cum laude with Phi Beta Kappa honors.[1] At the University of California, Irvine, Faust earned her Master of Arts (M.A.) in Social Science in 1983. She completed her Doctor of Philosophy (Ph.D.) in 1985. Following her doctorate, she was awarded a National Institute of Mental Health (NIMH) Postdoctoral Fellowship in Quantitative Psychology at the University of Illinois, Urbana-Champaign (1985–1987).[1]
Academic Career
[edit]Early Academic Roles (1987–2001)
[edit]Faust began her academic career as an Assistant Professor in the Department of Sociology at The American University (1987–1988). She then moved to the University of South Carolina, where she served as an Assistant Professor (1988–1993) and was later promoted to Associate Professor (1993–2001).[3] During this period, her research focused on developing methodological approaches to social network analysis and exploring the relationships between network structures and social processes.
University of California, Irvine (2001-Present)
[edit]In 2001, Faust joined the University of California, Irvine as an Associate Professor in the Department of Sociology. She was promoted to full Professor in 2009, a position she continues to hold.[1] At UCI, she has played a key role in advancing the field of social network analysis through her research and mentorship of graduate students.[2]
Significant Contributions
[edit]Social Network Analysis
[edit]Katherine Faust and Stanley Wasserman's Social Network Analysis: Methods and Applications introduced mathematical and statistical methods that transformed the study of social networks.[4] One of the key ideas in the book is that social relationships are not just individual connections but part of larger structures that shape behavior. The book emphasized that networks have both local and global properties, meaning that individual relationships influence, and are influenced by, the overall structure of a network (such as a school or workplace). This perspective challenged earlier sociological approaches that focused primarily on individuals or groups without considering how patterns of relationships could be systematically analyzed. By introducing formal mathematical models, the book allowed researchers to quantify social influence, power, and cohesion in ways that were not possible before. These methods made it easier to compare different networks, predict how they change over time, and test theories about why certain structures emerge.[5]
Another contribution of the book was its focus on statistical models for network analysis, particularly Exponential Random Graph Models (ERGMs). These models provided a way to test hypotheses about how networks form, rather than just describing them. Before this, social network studies were often purely observational, mapping out relationships without a formal way to explain why they looked the way they did. Faust and Wasserman introduced methods to analyze centrality (who holds power in a network), structural balance (how stable relationships are), and clustering (how subgroups form). Their work has tremendously improved our understanding of how social connections influence upward mobility and economic inequality by highlighting the structural constraints on individuals’ opportunities. Their work also helped bridge the gap between graph theory and empirical social science, making network analysis more rigorous and widely applicable.[5] Today, these methods are used in fields ranging from epidemiology to political science, helping researchers study how diseases spread, how companies innovate, and how social movements gain momentum.[6][7][8]
Network Methodology Theory
[edit]Faust argues that understanding social networks requires sophisticated mathematical tools that can capture both local and global network properties. This perspective has led to the development of new analytical methods that bridge the gap between abstract mathematical models and empirical social research. Her work demonstrates that network analysis requires attention to multiple levels of structure simultaneously. While traditional approaches often focused either on individual actors or complete networks, Faust’s methods enable researchers to examine how local patterns aggregate to create larger network structures, and how these structures in turn constrain local interactions. Additionally, her methodological advancements have allowed for better visualization and interpretation of network data, improving the accessibility and applicability of network analysis in various disciplines.[9] These methods have also been applied to understanding social stratification by analyzing how individuals' network positions shape their access to opportunities, reinforcing or challenging existing social inequalities.[10]
Comparative Network Analysis Framework
[edit]A significant theoretical contribution of Faust’s work is her framework for comparing networks across different contexts and scales. This framework addresses a fundamental challenge in network analysis: how to meaningfully compare networks that differ in size, density, and composition. Her approach considers both structural and compositional features, enabling researchers to identify universal properties while accounting for context-specific variations. Faust’s comparative framework emphasizes the importance of size-independent measures that allow comparison of networks regardless of their size. She developed methods for identifying structural equivalence across different types of networks while incorporating social and cultural context into network comparisons. Additionally, her comparative framework has been instrumental in studying social cohesion, organizational efficiency, and cross-cultural differences in network structures, offering valuable insights into how network properties manifest in different societies and institutional settings.[11] In social stratification research, this framework has been applied to compare how social mobility is influenced by network structures across different socioeconomic groups, demonstrating how the density and composition of social ties impact access to resources and upward mobility.[12]
Local Structure Theory
[edit]Faust’s work on local network structures has revolutionized understanding of how social networks form and evolve. She argues that local patterns, particularly triads, serve as fundamental building blocks for larger network structures. Her research on triadic configurations has revealed how relationships among three actors follow predictable patterns that reflect underlying social processes. The concept of transitivity in social networks, where friends of friends tend to become friends, has been particularly influenced by Faust’s work. She has demonstrated how structural balance theory can explain the tendency for certain triadic configurations to be more stable than others. Her research on social closure has illuminated the processes by which open triads become closed, providing insight into how larger network structures emerge from local interactions. Furthermore, her work has emphasized the role of local structures in determining network resilience, highlighting how micro-level interactions contribute to the overall stability and adaptability of networks in dynamic environments. This has direct implications for social stratification, as closed networks can reinforce exclusionary social practices, limiting access to new opportunities for marginalized groups while ensuring the persistence of privilege within well-connected social circles. [13]
Methodological Innovations
[edit]Faust’s work on centrality in affiliation networks has provided new ways to understand power and influence in social structures. Her methods account for multiple types of relationships simultaneously, considering both direct and indirect connections while incorporating the strength of ties into centrality calculations. This approach has transformed how researchers analyze power dynamics in complex social networks. In the realm of statistical modeling, Faust has developed sophisticated probability models for network structures. These models enable researchers to test hypotheses about network patterns and analyze network evolution over time. Her statistical innovations have particularly influenced how researchers study dynamic networks and temporal changes in social structures. Moreover, Faust’s contributions to computational social science have helped refine algorithms for large-scale network analysis, improving the ability to detect hidden structures within vast datasets. These advancements have been crucial in studying how structural advantages, such as having a high degree of centrality, enable certain individuals or groups to maintain privileged positions within social hierarchies by accumulating influence and social capita.[14]
Publications
[edit]Books
[edit]Social Network Analysis: Methods and Applications (1994, revised edition 1997) – Co-authored with Stanley Wasserman, this book is considered one of the most comprehensive texts on social network analysis.[15][4][1] Katherine Faust and Stanley Wasserman provided a comprehensive introduction to the methodology and applications of social network analysis. The book covers fundamental concepts such as network structure, centrality, cohesion, and statistical models for network data. It has become a foundational text in the field, widely used in both academic and applied settings.[15][4] The book has been referred to as "the bible of Social Network Analysis.[4]"
Selected Articles and Book Chapters
[edit]"A puzzle concerning triads in social networks: Graph constraints and the triad census" (Social Networks, 2010)[1][16]
"Animal Social Networks" (Sage Handbook of Social Network Analysis)[1][17]
"Very local structure in social networks" (Sociological Methodology, 2007)[1][18]
"Comparing social networks: Size, density and local structure" (Metodološki Zvezki, Advances in Methodology and Statistics, 2006)[1][19]
"Using correspondence analysis for joint displays of affiliation networks" (in Models and Methods in Social Network Analysis, 2005)[1][20]
"Centrality in affiliation networks" (Social Networks, 1997)[1][21]
References
[edit]- ^ a b c d e f g h i j k "Curriculum Vitae". doi.org. doi:10.21107/sml.v3i1.7307.s882. Retrieved 2025-02-23.
- ^ a b "Katherine Faust | D-Lab". live-dlab.pantheon.berkeley.edu. Retrieved 2025-02-23.
- ^ "UC Irvine - Faculty Profile System". www.faculty.uci.edu. Retrieved 2025-02-23.
- ^ a b c d "Katherine Faust's schedule for Sunbelt Conference 2019". sunbeltconference2019.sched.com. Retrieved 2025-02-23.
- ^ a b Wasserman, Stanley; Faust, Katherine (1994). Social Network Analysis: Methods and Applications. Structural Analysis in the Social Sciences. Cambridge: Cambridge University Press. ISBN 978-0-521-38707-1.
- ^ Yu, Fei; El-Zaatari, Helal M.; Kosorok, Michael R.; Carnegie, Andrea; Dave, Gaurav (2024-01-23). "The application of exponential random graph models to collaboration networks in biomedical and health sciences: a review". Network Modeling Analysis in Health Informatics and Bioinformatics. 13 (1): 5. doi:10.1007/s13721-023-00439-w. ISSN 2192-6670.
- ^ Ghafouri, Saeid; Khasteh, Seyed Hossein (2020). "A survey on exponential random graph models: an application perspective". PeerJ. Computer Science. 6 e269. doi:10.7717/peerj-cs.269. ISSN 2376-5992. PMC 7924687. PMID 33816920.
- ^ Box-Steffensmeier, Janet M.; Campbell, Benjamin W.; Christenson, Dino P.; Morgan, Jason W. (October 2019). "Substantive implications of unobserved heterogeneity: Testing the frailty approach to exponential random graph models". Social Networks. 59: 141–153. doi:10.1016/j.socnet.2019.07.002. S2CID 85510094.
- ^ "Katherine Faust's research works | University of California, Irvine, CA (UCI) and other places". ResearchGate. Archived from the original on 2024-01-16. Retrieved 2025-02-23.
- ^ Jalali, Zeinab S; Introne, Josh; Soundarajan, Sucheta (2023-01-04). "Social stratification in networks: insights from co-authorship networks". Journal of the Royal Society Interface. 20 (198). doi:10.1098/rsif.2022.0555. PMC 9810428. PMID 36596457.
- ^ Faust, Katherine; Skvoretz, John (2002). "Comparing Networks Across Space and Time, Size and Species". Sociological Methodology. 32 (1): 267–299. doi:10.1111/1467-9531.00118. ISSN 1467-9531.
- ^ Ramos, Valentina; Pazmiño, Pablo; Franco-Crespo, Antonio; Ramos-Galarza, Carlos; Tejera, Eduardo (2022-01-01). "Comparative organizational network analysis considering formal power-based networks and organizational hierarchies". Heliyon. 8 (1): e08661. Bibcode:2022Heliy...808661R. doi:10.1016/j.heliyon.2021.e08661. ISSN 2405-8440. PMC 8753125. PMID 35036592.
- ^ Faust, Katie (2006-01-18). "Very Local Structure in Social Networks".
{{cite journal}}: Cite journal requires|journal=(help) - ^ Faust, Katherine (1997-04-01). "Centrality in affiliation networks". Social Networks. 19 (2): 157–191. doi:10.1016/S0378-8733(96)00300-0. ISSN 0378-8733.
- ^ a b "APA PsycNet". psycnet.apa.org. Archived from the original on 2023-01-02. Retrieved 2025-02-24.
- ^ Faust, Katherine (2010-07-01). "A puzzle concerning triads in social networks: Graph constraints and the triad census". Social Networks. 32 (3): 221–233. doi:10.1016/j.socnet.2010.03.004. ISSN 0378-8733.
- ^ sso.sagepub.com https://methods.sagepub.com/hnbk/edvol/the-sage-handbook-of-social-network-analysis/chpt/animal-social-networks. Retrieved 2025-02-24.
{{cite web}}: Missing or empty|title=(help) - ^ Faust, Katherine (2007). "Very Local Structure in Social Networks". Sociological Methodology. 37 (1): 209–256. doi:10.1111/j.1467-9531.2007.00179.x. ISSN 1467-9531.
- ^ Faust, Katherine (2006-07-01). "Comparing social networks: Size, density, and local structure". Advances in Methodology and Statistics. 3 (2). doi:10.51936/sdbv3216 (inactive 1 July 2025). ISSN 1854-0031.
{{cite journal}}: CS1 maint: DOI inactive as of July 2025 (link) - ^ Faust, Katherine (February 2005), "Using Correspondence Analysis for Joint Displays of Affiliation Networks", Models and Methods in Social Network Analysis, Cambridge University Press, pp. 117–147, doi:10.1017/CBO9780511811395.007, ISBN 978-0-521-80959-7, retrieved 2025-02-24
- ^ Faust, Katherine (1997-04-01). "Centrality in affiliation networks". Social Networks. 19 (2): 157–191. doi:10.1016/S0378-8733(96)00300-0. ISSN 0378-8733.