Network analysts have long used personal network visualizations to conduct qualitative interviews with respondents about the impact of their social context on outcome variables. This network ethnographic approach is often combined with quantitative analyses across respondents to generate a more complete and nuanced understanding of ways social context may be used as an intervention. Since the expansion of AI in 2022 there are now many tools for easily translating interview recordings to text for systematically analyzing the transcripts. We use a manual text analysis approach combined with generative AI to analyze 60 personal network interviews of former smokers regarding their efforts to stay quit, and to assist those in their networks to also quit smoking. We compare the concepts generated through manual text analysis versus using AI tools, and demonstrate findings from the interviews that would be difficult or impossible to discover using only network models of quantitative data.

The paper and presentation will discuss a new qualitative method to measure and evaluate the impacts of “impact networks” (D. Ehrlichman). How can we create useful and reliable evidence of the actual positive, negative, intended and unintended changes caused by networks of individuals or organizations?

Impact evaluation research and practice offer quantitative experimental and quasi-experimental methods to measure attributable treatment effects, on the one hand, as well as many approaches that focus on theory-building, participation of respondents and the use of qualitative data, on the other hand. The Qualitative Impact Assessment Protocol (QuIP) developed by the University of Bath attempts to bring together the strengths of the latter type of evaluation methods and offers a much-needed level of standardization. It has been utilized in numerous evaluations in diverse fields such as development, education or social services, sometimes to complement experimental designs, and has much potential for measuring network impacts in the future as well.

The presentation will discuss the author’s ongoing work with QuIP and the Causal Map App to analyze and visualize the causal statements gathered in interviews with network members based on two completed evaluations of networks of organizations. It will focus on methodological aspects such as the role of blindfolding, inter-coder reliability, transparency and representativity. Current attempts to utilize AI for coding and conducting interviews will also be discussed. In all, the presentation will aim to offer valuable insights and inspire discussion of the strengths and limitations of qualitative methods to evaluate and research networks.

Participatory water governance involves collaboration among government agencies, non-governmental organizations, water users, and academic institutions to address water-related issues. This collaboration may manifest in various forms, such as conducting research, managing water resources, and formulating policy. Effective stakeholder coordination and cooperation are typically operationalized through participatory structures, including water research/practice networks. The Arizona Water for All (AW4A) initiative aims to establish a multistakeholder network in the format of a Community of Practice—a group of individuals who share common interests, face similar challenges or are passionate about water security—to support water-insecure communities and facilitate participatory water research and decision-making throughout Arizona. This study introduces a mixed-methods framework for developing participatory initiatives and evaluating the enhancement of linkages (such as network ties) among various stakeholders across different regions—south, central, and north Arizona—within the AW4A Network initiative. We integrated Social Network Analysis (SNA) with ethnographic research methodologies to develop this comprehensive framework. Ethnography, which involves in-depth immersion in the context being studied, provided important insights into the complexity of social interactions that SNA techniques captured. By employing this combined approach, we were able to provide a more nuanced interpretation of the network data by validating the patterns and results obtained through SNA against the insider knowledge gathered from ethnographic interviews. We contributed to the research and methodological design of mixed-methods studies to explore the formation and dynamics of collaborative water networks.

We present an application of a mixed methods social network study in the field of collaborative environmental management. Effective environmental management generally requires coordination and collaboration between diverse heterogenous actors. However, communication and working together towards a common objective are often difficult in these settings. Actors involved need to overcome tacit boundaries and reconcile different viewpoints to reach a shared understanding on the environmental problem and envisioned solution(s). Thus, based on the notion that meaning and structure in social networks are co-produced, we assume that a common narrative can serve as a base for a shared understanding and ask the question what types of relationships influence the emergence of a common narrative. This presentation will showcase a mixed methods network study and underlying conceptual ideas and empirical implementation. We apply the idea of narrative congruence, which relates to the similarity of narrations that actors tell, to investigate the effects of the types of relationships between two actors as well as specific leadership roles using an Exponential Random Graph Model. Furthermore, we highlight how we combined qualitative and quantitative data meaningfully and what findings we got from this approach. Lastly, we would like to reflect on our taken mixed methods approach highlighting opportunities and challenges from which others could learn for the future.

Key words: collaboration, narratives, leadership, ERGM

In recent years, the literature has proposed different studies that combine the analysis of node-level attributes alongside topological information of the network. These proposals range from hierarchical clustering algorithms including relational constraints to communities in the context of Subgroup Discovery, and data-driven probabilistic methods on multilayer networks. While these methods have been proposed to integrate structural and attribute-based information, achieving a balanced and coherent representation remains challenging. In this work, we apply DISTATIS, a three-way multidimensional scaling technique, to jointly analyze network topology and node attributes. Through simulations on networks with different attribute types, our results demonstrate that DISTATIS effectively captures the coherence between the attributes (qualitative and quantitative) and network structure. This approach offers a valuable tool for extracting complex relationships in real-world networks where both structural and attribute-driven factors are crucial.

How do interiorized learning patterns from art school shape the ways artists work? The digital turn constitutes a process of institutionalization of artistic practices. Previous studies show that art studios are spaces for cultural and social capital formation. Moreover, current practices are linked to learning conditions and previously embodied, distributed and extended cognitive experiences. Digital and analog tools are key epistemic objects that reproduce patterns of artistic gossip among artists and beyond the studio walls. We claim that video-aided ethnography shows how artistic networking shapes the cognitive process of artistic practice. The sample consists of 45 interviewed artists from different generations and four visual disciplines, with more than 10 years of professional experience, balanced by age and gender. The mixed methods design is a video aided ethnography with interviews, observation, participatory photography, video elicitation, SNA, and focus groups. Qualitative data analysis includes grounded theory, thematic analysis, conversational analysis, multimodal analysis with ELAN and quantitative analysis of egonetworks with Egonet/Ucinet. Centrality and compositional measures formalize the accumulation of successful relationships. The visualization of the network is a useful tool to produce more specific discursive data from the subjects, as well as an objective contrast to their expectations on how they socialize among their colleagues and friends. At the same time, artistic gossip from ethnographic observation and interviews contextualizes the networking patterns. Structural factors permeate the micro-level so that networking becomes a key component of the creative process.

During and up to the present moment in the academic formation journey, I have recognized myself as a relational sociologist, currently interested in coordination of collective action engaged in climate change. I have designed the doctoral research as a relational ethnography to investigate how the unfolding of collaborative relationships among collective actors shapes the identization process during a wave of mobilization. Between 2019 and 2024 the research design followed a parallel mixed-methods approach on the case of Fridays for Future in Italy. Participant observation and thick descriptions of meetings have led to the investigation of the decision-making procedures shaped by ties’ dynamics. Two survey’s rounds have produced longitudinal and multi-layer whole and ego-network data, further enriched by in-depth interviews capturing alter-alter ties, which have allowed for an analysis of the roles within the social structure. Notably, in-depth interviews have facilitated the emergence of the grassroots collective actors’ narratives which, through within-case and cross-cases coding of relational mechanisms, have reconstruct the pathways of modes of coordination traversed by the grassroots collective actors. The paper aims to unfold the choices, difficulties and solutions faced during the research process, engaging with studies in which network is both a theoretical concept and an analytical tool. Thus, the themes explored concern: the fluidity of the boundaries delineated by co-attendance ties; the relational ingredients chosen, found, and re-evaluated as relevant for the identization process; the relational mechanisms that shape the collective actors’ pathways; and finally, the dynamics of trust relationships within the research process itself.

Scientific literature shows that in Africa, women mostly exchange seeds between themselves. However, few studies have examined these inter-female exchanges and how relationships between women influence them. Yet, these practices play a central role for women, engaging social, economic and food dynamics. To shed light on the dynamics of cowpea circulation and exchange in Senegal, we propose to combine the ethnographic method with social network analysis (SNA).

Our fieldwork was based on ethnography over two cultural seasons, combining semi-structured interviews and participant observation. This approach helps to highlight peripheral exchange dynamics among women during the harvest, practices that are difficult to reveal by conventional quantitative survey methods. At the same time, SNA provides a quantitative perspective by mapping relationships and interactions, making visible the dynamics of reciprocity and centrality in these exchanges. Combined with the ethnographic approach, it is also a level of abstraction that allows for a deeper understanding of how kinship, alliance, residence, but also domestic cycles participate to organizing these exchanges.

The objective of this study is twofold. First, it seeks to highlight the complexity of social dynamics, power relations, and inequalities involved in the circulation of cowpeas, which are often obscured by gender disparities through a male-female opposition. Second, it offers a deeper reflection on the methodological hybridization between ethnography and network analysis as a tool for analyzing female moral economies in an agricultural context.

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another perspective built on the notion of local dominance, where low-degree nodes are assigned to the basin of influence of high-degree nodes, and design an efficient algorithm based on local information. Local dominance gives rises to community centers, and uncovers local hierarchies in the network. Community centers have a larger degree than their neighbors and are sufficiently distant from other centers. The strength of our framework is demonstrated on synthesized and empirical networks with ground-truth community labels. The notion of local dominance and the associated asymmetric relations between nodes are not restricted to community detection, and can be utilised in clustering problems, as we illustrate on networks derived from vector data.

The increasing involvement of academics and intellectuals in the Italian public debate, often on topics that go beyond their original expertise, raises questions about the role of expert knowledge and its interaction with the media field. Building on this premise, we propose an analysis of the relational and content dynamics shaping public debate of experts and intellectuals in their digital interactions.

This study adopts a mixed-methods approach that integrates Social Network Analysis (SNA) with statistical text analysis techniques. Specifically, we will examine the network of interactions among public figures in the Italian context on Social Network X, where connections — defined through mentions, replies, and retweets — will be labeled according to thematic categories extracted via cluster analysis using the Reinert method, applied to the corpus of posts published by the authors.

In our view, this analytical strategy allows us to explore the relationship between network structure and the specialization or transversality of discourses, providing a framework to assess whether digital public debate tends to fragment into thematically defined communities or, conversely, whether more hybrid and interconnected configurations emerge.

From a methodological perspective, this study addresses key challenges in network construction, including the thematic characterization of ties, the management of intersections between multiple discursive categories, and the definition of criteria for measuring the thematic specialization or transversality of specific relational configurations. The ultimate goal is to determine whether the structure of digital interactions reinforces the polarization of public debate or, alternatively, facilitates more fluid and interconnected dynamics.

This study addresses a critical methodological gap in social network analysis by developing a novel analytical framework that adapts the "prescribing cascade" concept from medical science to analyze evolutionary complexity in R&D networks in science and technology policy implementation. While existing SNA approaches capture network structure at discrete timepoints, they lack tools for analyzing cascading effects of policy interventions across multiple stages of network evolution.

The proposed mixed methods framework integrates four matrix-based analytical techniques: (1) Basic adjacency matrices capturing initial R&D network relationships, (2) Weighted adjacency matrices quantifying relationship directionality across intervention stages, (3) Policy intervention matrices tracking new institutional relationships introduced by each policy intervention, and (4) Network evolution matrices measuring changes in key network properties including density and average path length across cascade stages. We operationalize this framework by developing weighted matrices that capture the nature of relationships between actors (universities, industries, government agencies, coordination bodies, and evaluation institutions), tracking from Stage 0 (initial network) through Stage 1 (first cascade) to Stage 2 (second cascade).

Our methodological contribution demonstrates how tracking matrix transformations and resulting network property changes can reveal cascade patterns in policy interventions. This matrix-based methodology offers researchers a systematic way to analyze how initial policy interventions designed to enhance R&D coordination can paradoxically increase network complexity. Beyond R&D networks, the framework can be applied to various complex systems where policy interventions trigger institutional adaptations, advancing both theoretical understanding of policy-induced network evolution and practical methodological tools for analyzing cascade effects in social networks.

The collection and analysis of empirical temporal contact networks have experienced remarkable growth over the past two decades. Sociopatterns, a research collaboration, has gathered high-temporal resolution data on physical proximity and face-to-face interactions across a wide range of social contexts (J. Stehlé et al., 2011). These datasets have paved the way for a new wave of quantitative studies on individual and social behaviors. This advancement is particularly significant when combined with sociological and psychological metadata collected through participant surveys, as demonstrated in studies conducted during conferences (M. Génois et al., 2019).

Disentangling phenomena that require social explanations from those that do not is a complex task, necessitating the development of new analytical methods. Further research has shown that certain behavioral characteristics appear to be universal and can be explained by simple mechanisms (R. Masoumi et al., 2024). In this context, we identify among all determinants measured onto the empirical face-to-face networks, the ones that can be correlated with social and psychological behavior, from others with universal properties. Moreover, by leveraging the diversity of social contexts studied using consistent data collection methods, alongside statistical tests and renormalization processes to assess the relevance of behavioral observables, we compare these determinants across different contexts through longitudinal studies. For instance, this method unveils that loyalty, the repetition of interaction, is correlated to individual properties and social context.

This method, by integrating network features with metadata analysis, provides researchers with a more comprehensive framework for analyzing and explaining social behaviors in face-to-face interaction networks.

The goal of the project is to identify key patterns of relationships that best predict strong social support and positive emotional well-being and thriving for Black and Indigenous college students. Using a quantitative critical and social network analysis approach (QuantCritSNA), this project consists of a mixed methods secondary data analysis on the (n=300) supportive people and spaces of 22 college students, collected at one large public land grant university in the Midwestern United States, to address the following aims: 1) Identify specific social network characteristics and compositions that best predict strong well-being support, accounting for effects due to the students (egos), whom they turn to for support (alters), the type of support offered, and contextual variables such as location of support; and 2) determine the processes involved in the creation and maintenance of strong well-being support both on and off campus. Employing a mixed methods social network analysis (Dominik et al., 2020) allows for the testing of social and relational mechanisms that support college student well-being, combined with the narrative evidence of the process of well-being support, ultimately building an evidence-based intervention development foundation, which as a necessary precursor to network intervention research and implementation. As such, this research will fill a research gap by identifying network mechanisms to provide greater evidence of associations between social networks, social support, and college student emotional well-being, which can guide institutional practice and policy that targets specific institutional resources and interactions that promote equitable well-being for all.

Aims: We sought to understand how structural configurations of personal social networks collectively explain perceived social support among older adults with advanced cancer. We used Qualitative Comparative Analysis (QCA), a systematic approach for comparing multiple cases to discern how different combinations of factors jointly produce an outcome. Rather than isolating single variables, QCA treats each condition as a set and uses set-theoretic principles to reveal which combinations of set memberships reliably account for the outcome. Designed for small to medium samples, QCA uncovers real-world complexities overlooked by conventional statistical approaches.

Methods: Fifteen older adults with advanced cancer participated in the study. Each participant was guided through completing a personal network chart—a visual map showing the key individuals involved in their health and well-being (1). On this chart, participants specified how different people (e.g., spouse, family, friends, neighbors) connect both to them and to one another. They also completed the Berkman-Syme Social Network Index, a tool that measures perceived social support.

We conducted a fused Mixed-Methods analysis (2,3), integrating insights from personal network visualizations (qualitative data) with quantitative network analysis. This approach enabled us to identify various structural patterns within personal networks. We then applied QCA to determine which sets of configurations predict higher social support. For QCA, we established four theoretically grounded conditions: (1) existence of a network core (one or more crucial individuals—such as a spouse—connected to many others), (2) existence of a dominant cohesive circle (a tightly interconnected cluster, for instance, of family members), (3) the presence of segregated clusters or isolated individuals (e.g., friends, colleagues, extended family), and (4) the size of the inner circle in the network chart. QCA helped us pinpoint consistent combinations of these conditions that lead to higher perceived social support.

Using the intermediate solution—a QCA approach that employs theoretically guided assumptions while avoiding extreme simplifications—we found that patients tend to perceive higher social support when any of the following configurations is present: (1) the network is less segregated, featuring fewer isolated clusters and more interconnected ties; (2) a spouse is part of the network alongside a larger inner circle; or (3) the network has a larger inner circle even without cohesive clusters. These configurations demonstrated high consistency of 0.85 (measuring how reliably a set of conditions is associated with the outcome, Pi consistency: 0.81) and coverage of 0.81 (the proportion of outcome cases that a particular set of conditions explains).

In addition, necessity analysis showed that most instances of higher support had either a less segregated network or a larger inner circle (relevance of necessity: 0.553, necessity coverage: 0.783).

Conclusion: This study demonstrates that less segregated networks or the existence of a robust inner circle predict a higher sense of social support among older adults with advanced cancer. Notably, even networks without overall cohesion patterns still achieve high levels of support with a robust inner circle. These findings can inform the adaptation of network-building interventions to reduce isolation and strengthen social support.