This workshop introduces the major streams of social network theory, taking a conceptual view grounded in and contrasted with the broader landscape of social theory. This is not a course on network methodology, although mathematical concepts such as centrality and structural equivalence are discussed. The workshop is intended as a survey of the major conversations network researchers from the social sciences are engaged in. It is suggested for those new to the network perspective who are interested in a map of the theoretical landscape. The workshop does not use any software or data. Network Theory -- Outcomes of network variables/mechanism Here we consider network theorizing in both the social capital literature (e.g., weak ties, structural holes, social resource theory) and the contagion literature (e.g., interpersonal influence, diffusion of innovation). We examine how concepts like centrality and core-periphery structures are interpreted in these different contexts. Topics include multiple levels of analysis, theoretical network mechanisms, and social cognition (perceived ties). Themes include the interplay of node characteristics and network structure, as well as … Theory of Networks -- Antecedents of Network Variables This section deals with theories of tie formation, why networks have the shapes they do, and why actors occupy the network positions they do. Topics include homophily, preferential attachment, mechanisms of choice and opportunity, balance theory, etc.

The goldfish package offers tools for applying statistical models to relational event data. The study of relational events is growing in social network research, driven by the increasing availability of data. For example, data collected from digital traces of individuals’ interactions —such as communication exchanges, transactions, and collaboration— provide in-depth details regarding the timing or sequence of relational actions between actors. The workshop provides an introductory theoretical overview from a social science perspective, complemented by a hands-on tutorial (as time permits) on the different models implemented in the package: * Dynamic Network Actor Models (DyNAM): Investigate relational event models as an actor-oriented decision process. - rate: Actors compete to create the next relational event (Hollway, 2020). - choice: The active actor chooses the receiver of the event from among the same (Stadtfeld and Block, 2017) or a different set of nodes (Haunss and Hollway, 2023). - choice_coordination: The creation of coordination ties as a two-sided process (Stadtfeld, Hollway, and Block, 2017a), as in studies analyzing agreements between countries. * Dynamic Network Actor Models for interactions (DyNAMi): Investigate dynamics of conversation groups and interpersonal interaction in different social contexts from an actor-oriented perspective (Hoffman et al., 2020), as in studies using social sensors. - rate: Actors compete to join or leave groups. - choice: The active actor chooses the group to join. * Relational Event Models (REM): Investigate relational event models as a tie-oriented process (Butts, 2008), taking into account right-censoring (Stadtfeld, Hollway, and Block, 2017b). Prerequisites: Participants should be familiar with R and model-based statistical inference (such as logistic regression). Please bring a laptop with the R software environment, the goldfish package installed, and its dependencies. More information about the package and installation is available at: https://github.com/stocnet/goldfish References: Butts, Carter. 2008. “A Relational Event Framework for Social Action. ”Sociological Methodology 38 (1): 155–200. Haunss, Sebastian, and James Hollway. 2023. “Multimodal Mechanisms of Political Discourse Dynamics and the Case of Germany’s Nuclear Energy Phase-Out.” Network Science 11 (2): 205–23. https://doi.org/10.1017/nws.2022.31. Hoffman, Marion, Per Block, Timon Elmer, and Christoph Stadtfeld. 2020. “A Model for the Dynamics of Face-to-Face Interactions in Social Groups.” Network Science 8 (S1): S4–25. https://doi.org/10.1017/nws.2020.3. Hollway, James. 2020. “Network Embeddedness and the Rate of Water Cooperation and Conflict.” In Networks in Water Governance, edited by Manuel Fischer and Karin Ingold, 87–113. Cham: Palgrave MacMillan. https://doi.org/10.1007/978-3-030-46769-2_4. Stadtfeld, Christoph, and Per Block. 2017. “Interactions, Actors, and Time: Dynamic Network Actor Models for Relational Events.” Sociological Science 4 (14): 318–52. https://doi.org/10.15195/v4.a14. Stadtfeld, Christoph, James Hollway, and Per Block. 2017a. “Dynamic Network Actor Models: Investigating Coordination Ties Through Time.” Sociological Methodology 47 (1): 1–40. https://doi.org/10.1177/0081175017709295. ———. 2017b. “Rejoinder: DyNAMs and the Grounds for Actor-oriented Network Event Models.” Sociological Methodology 47 (1): 56–67. https://doi.org/10.1177/0081175017733457. Length: 3 hours Participants: 30

The development of the social network perspective has progressed rapidly, evolving from " random pieces sitting out in the midst of the desert (forest?)” (Mullins & Mullins, 1973, p. 264) to a field of study that is " Finally, there is reason to be hopeful since the field of social network analysis is currently very “hot,” growing at an amazing rate.” (Freeman, 2004, p. 167). Much has changed since these early observations, as we will see through recent bibliometric studies (e.g., Espinosa-Rada & Ortiz, 2022; Maltseva & Batagelj, 2020, 2021, 2022, 2024). The history of social network analysis reveals key groups and institutional milestones that have driven its development and consolidation, including events like the Sunbelt conferences, the establishment of INSNA, and the launch of network-focused journals (Freeman, 2004; Scott, 2011). The field of network science (Brandes et al., 2013) has also significantly influenced this trajectory. Furthermore, we aim to contextualize phenomena such as the “physicist invasion” and the more recent “data scientist invasion,” as well as the emergence of advanced statistical models in social network research to identify the contribution of modern interdisciplinary trends. The field continues to be shaped by a vibrant community of practitioners, as illustrated in resources like the Knitting Networks podcast. By revisiting the history of the social network perspective, participants will gain insight into the origins of key concepts such as homophily, structural balance, cliques, or roles. They will also explore how different research groups have used social network approaches to address core questions in the social sciences. By understanding the field’s evolution, participants can more fully appreciate the opportunities and challenges the social network perspective faces today, leveraging historical insights to shape future research.