Purpose: This study investigates the possible intersection between formal academic collaborations, measured through co-authorships, and informal interactions, assessed through joint organization of conferences, events, and/or teaching, among scholars of Early Women Sociologists (EWS). Part of the broader project “Gendering Sociology: Proposal for Research and Teaching”, this paper aims to recover the intellectual contributions of EWS by mapping formal and informal academic networks in this field, verifying if informal collaborations foster structured scholarly output or remain detached from formal research.

Methods: The mapping process was structured in three phases: (1) Desk-based identification of scholars, conducted through a multilingual scoping review across academic databases and research networks to identify EWS. (2) Administration of a questionnaire to international scholars and experts, to collect two types of ego-alter data: the informal relations, and the inspiring EWS. (3) Collection of official bibliographic records from Scopus and Ebsco repositories for respondents and alters.

Hence, we analyze relational data using a multilayer network approach. Specifically, examine three layers: the “formal” academic interactions captured by the co-authorship network (i.e. Layer 1); the “informal” collaborations network, or the overall sociometric structure derived from questionnaire responses (i.e. Layer 2); and finally, the EWS layer, which determines the foundational knowledge base for these scholars (i.e. Layer 3).

Contributions: The findings reveal that, while some international EWS have influenced sociological thought, the research community remains highly fragmented, with informal collaborations largely confined to small, self-referential groups. Notably, informal scholarly interactions rarely translate into formal co-authorships, suggesting the presence of structural barriers that hinder the transformation of intellectual exchange into sustained research output.

In 1994 Gibbons [3] claimed that knowledge production shifted from its traditional form to a new synergy between academic and industrial actors. Although numerous studies have shown the strengthening of academia-industry ties, many also noted resistance to industrial logic and the effort to maintain clear boundaries [2, 6].

The field of AI, with its largely mixed composition, appears emblematic of this reconfiguration.

If initial discoveries were made in academia, many of the recent advancements are industry driven. The private sector, due to its substantial resources and its access to large databases, attracts most new talent, produces the most groundbreaking models and consequently dominates the field [1,4].

If elements such as the creation of industrial laboratories, participation in conferences, or contributions to the scientific literature highlight the rapprochement between the two actors, Raimbault [5] points out that, while some practices align, the goals and subsequent artifacts in which researchers invest remain differentiated. In other words, academics focus on papers as they produce value in their field, and industrials will focus on providing working code and models.

Considering the industrial domination in the field and the many convergences with academia, can we observe the emergence of a unified system of knowledge production and diffusion?

Using the PapersWithCode database as well as the OpenAlex and Github APIs, we collected extensive information about research projects including a code repository and an academic paper. Our findings suggest that, while some similarities emerge, industrial studies largely deviate in their topics, diffusion, and reception by scientific communities.

First, looking at the citations, we noted a slight industrial edge. Although industrial articles receive more attention, we noted that they also were significantly less likely to be fully published (12.3% when including an industrial author and 24% when fully academic). Furthermore, if published, industry papers have a longer time to publication (¯x ≈ 0.97 years for academic papers and ¯x ≈ 1.17 for industrials) and only focus on the top journals, suggesting an instrumental use of the publication system. Moreover, using Uzzi’s [7] method, we observed a higher tendency towards classical topic combinations for industry papers. Finally, employing a time series clustering method, we noted that academic articles are overrepresented in the clusters with exponential citation growth.

Regarding repositories, we noted significant differences. Industrials display higher diversity in programming language and some specificity, such as the usage of CUDA. Moreover, private researchers appear to invest more in the presentation and usability of the repository. This can be seen in several metrics such as the documentation length or the inclusion of” Code examples”. We also note a higher industrial popularity on Github as well as a faster accumulation of stars. But we paradoxically observed very little discrepancy in the maintenance of repositories, with even an academic edge in some metrics, such as the time to close issues.

Overall, our results show that, while more successful, industrial research has narrower interests and displays relatively instrumental use of the artifacts questioning the idea of a unified system of knowledge production.

Scientific collaboration, widely recognized as a key driver of research progress and innovation, has grown significantly over the years across all academic disciplines. This trend has been further strengthened by government policies at both national and international levels, which actively promote collaborative research initiatives. In this context, co-authorship serves as a concrete indicator of collaborative behavior among scholars and is commonly used as a proxy for measuring and analyzing research collaboration

While research topics and methodological approaches often differ between disciplines, there are, for example, communities of scholars that share common ground and have a distinctive pattern in their research interests.

While research topics and methodological approaches often vary across disciplines, there are also communities of scholars that can share common ground and exhibit distinctive patterns in their research interests.

A notable example of this can be found in Italy, where Economics—along with related fields such as Business, Management, and others—coexists with Statistics within the same macro research area (designated as "Area 13" by the Italian Ministry of University and Research). In many Italian universities, scholars from these disciplines are often hired within the same department and often have teaching duties within the same degree/PhD programs. This proximity reflects shared characteristics in departmental and institutional environments, as well as alignment with national strategies and policies on scientific production and research quality.

However, key questions arise regarding the potential convergence of scientific production mechanisms between these two large communities. Specifically, does this shared environment influence coauthorship behavior, shaping coauthorship structures, publication style, and productivity over time?

To address these questions, this studycontribution aims to conduct a comparative analysis of co-authorship networks in Management and Statistics, starting withfrom their network topology and examining similarities and differences in co-authorship dynamics

How can scientific collaboration be explained within a research centre? While much of the social network literature on scientific networks tends to focus on academic papers as a means to explore scientific relationships (Bellotti & Espinosa-Rada, 2025), early studies in the sociology of science and knowledge have highlighted the importance of social interactions in knowledge diffusion and production (e.g., Coleman et al., 1957; Crane, 1972; Mullins, 1972; Collins, 1998; White, 2004). To better understand the complexities of social relationships in scientific settings, this study examines how informal communication, scientific interests, specialization, and structural opportunities influence and constrain scientific collaboration.

This presentation presents a case study of scientific collaboration within a research centre focused on sustainability science. The data was collected by combining surveys (i.e., sociometric) and bibliometric data. We investigate the various mechanisms driving collaboration, including friendship, regular interactions, informal communication, and shared "lazy" time spent together. We also account for the influence of factors such as institutional affiliation, interests in different specialities, and conflicts between researchers. For our analysis, we employ stationary actor-oriented models to analyze multilayer networks (i.e., multiplex and multilevel). The primary objective of this study is to uncover how cross-layer effects shape the relationships perceived as scientific collaborations.

How do new academic organizations emerge in an institutional landscape that favors stability? This study explores the role of networks in shaping the rise of Computational Social Science Labs (CSS Labs)—interdisciplinary research hubs that bridge computer science and social science. Drawing on institutional entrepreneurship and network theories, this research examines how scholars positioned at the intersection of different academic communities (i.e., multivocal actors) play a key role in creating and leading these new organizational forms. The methodology employs two comprehensive datasets: (1) a large-scale co-authorship network mapping collaborations between social and computational scientists using Web of Science publication data, and (2) a newly constructed global dataset detailing the introduction, membership information, and research agendas of CSS Labs. The Bayesian Hierarchical Network Autocorrelation Model will be used to model on the relationship between collaboration network position and the likelihood of being a founder of a CSS Lab. Through network analysis, this study assess how increased cohesion between disciplines fosters the emergence of CSS Labs and how these labs, in turn, reshape collaboration patterns. The findings will contribute to scholarly understanding about the paradox of embedded agency by demonstrating how network positioning enables actors to recombine diverse institutional elements to create novel organizations. This research also engages with the concept of "cultural holes," integrating a knowledge-based perspective into brokerage theory. By revealing the relational and structural mechanisms underlying interdisciplinary organizational emergence, the study offers insights into how new academic institutions take shape amid evolving research landscapes in social science fields.

We explore hierarchies in knowledge production by analysing the extent to which institutions in the global South receive research funding, vis-à-vis their counterparts – and collaborators – in the global North. We use ESRC data on grants, excluding fellowships, from between 2015 and 2020, to create a dataset of collaborations that receive funding.

A social network analysis or SNA approach allows us to assess which institutions have more influence, based on their capacity to collaborate internationally. A core assumption of SNA is that that social ties matter because they influence behaviour or transmit information and resources. As such, using SNA allows to enhance our understanding of phenomena that emerge from the interaction of individuals or institutions, particularly outcomes that depend on 'social capital' or, more generally, on the form and quality of collaboration between actors.

Global South collaborations.

Unsurprisingly, we find a core-periphery structure in ESRC-funded projects involving international collaboration, with funding centred on elite UK universities and Global South partners in peripheral positions. But interestingly we find some Global South institutions in intermediary positions between the core and the periphery. We also find distinct clusters of partners generally each centred on an elite UK university but with distinctive Global South nationalities and distinctive project themes. The study adds depth to our understanding of international hierarchies of knowledge production.

Scientific collaboration has been recognized as an important relationship that facilitates the sharing of expertise and knowledge, significantly contributing to research advancement and innovation. Consequently, national and international policies promote such partnerships across various sectors and institutions. In this work, we investigate how co-citation, keywords co-occurrence, and co-authorship networks influence each other within three Italian Academic Communities (IAC): sociologists, statisticians, and Management scholars from 2012 to 2022. We collect data from the Italian Ministry of Education and Scopus and apply the Relational Hyperevent Model (RHEM) to analyze the collaboration networks of the IAC over time. Additionally, we introduce a new hyperedge covariate, the geometrically-weighted subset repetition (GWSR), as a smoothed version of the formerly defined subset repetition to capture the persistence of groups in a more parsimonious model. The analysis illustrates the complexities of scientific collaboration and differences in collaboration strategies among IAC.

Gender inequalities within academia are widespread. Women publish less, are less likely to hold prominent author positions, and their contributions are overlooked compared to men. In the Netherlands, women are increasingly represented at the doctorate level. But while women are as likely as men to start an academic publishing career after obtaining a doctorate, their publishing careers are shorter. This in part explains why women remain underrepresented in the professoriate.

To shed more light on the impact of gender in publishing careers of scholars, in this paper, we will investigate the role of gender in collaboration networks among political scientists and sociologists in the Netherlands (N>500). Do scholars prefer to work (i.e. co-publish) within same-sex collaboration teams? To what extent does previous track record (e.g. citation scores) and seniority influence decisions who to work with and is this impact conditional on the gender of the potential collaboration partners?

To answer these and other questions we collected the names and positions of faculty members for all political science and sociology departments in the Netherlands in the period 2021-2025. We enriched these web-scraped data with information on faculty member's gender, ethnicity, seniority, publishing careers, online presence, etc.

We constructed complete, longitudinal collaboration networks (consisting of over 50.000 ties) and tested our hypotheses with RSiena.

Global North-South disparities persist in science, yet our understanding of the mechanisms sustaining them remain limited. Focusing on North-South research partnerships, this study examines how the division of labor within medical research teams contributes to these disparities. We harvested article metadata from PLOS ONE alongside CRediT contributorship data, and then applied a new TF-IDF-based method to account for variation in the prevalence and distribution of contributor roles across authors. In linear probability models, adjusting for authors’ prior publication output and impact, gender, scientific age, medical specialization and TF-IDF adjusted contributor roles, we find that GS researchers are more likely to assume first authorships but have substantially lower representation in last and corresponding authorships compared to their GN team-mates. Subgroup analyses reveal that this regional disadvantage is most pronounced for researchers from Africa, Latin America, and Southeast Asia, while those from East and South Asia are underrepresented in all lead-authorship roles, including also first authorships. This pattern also holds across national income levels, with clear disparities observed between researchers from lower- and higher-income countries. We also find that while leadership roles generally increase the likelihood of assuming first-, last- or corresponding authorships, GS scientists with such roles remain less likely to obtain last authorships. These findings expose a consistent misalignment between contributions and authorship positions in North-South collaborations and highlight the need for experimental research to clarify the causal pathways through which these imbalances arise.

Network methods have played a central role in the study of think tanks and policy expertise. Political scientists have examined think tanks to explore elite cliques engaged in ideological projects within specific political parties, while sociologists have focused on how these organisations leverage cross-professional networks to frame and stabilise political-economic problems through ideologically palatable policy solutions. However, little is known about the intellectual life within the broader think tank community or how these organisations interact with one another. Existing research tends to take a metaphorical approach to think tank networks, often neglecting formal qualitative and quantitative network analysis.

This paper addresses these gaps by analysing British think tank citation networks to: (1) map the intellectual networks that generate ideas and evidence in British public policy; (2) assess community cohesiveness; and (3) identify intellectual authorities shared across the broader think tank community and within specific cliques. This also presents a novel approach to studying how knowledge regimes—the institutional arrangements that shape the production and use of policy expertise—are structured. By applying this methodological framework, the paper offers new insights into how think tanks are organised, how they interconnect, and what forms of knowledge are considered authoritative within the British knowledge regime

Scientific networks are key to understanding knowledge creation, and a rich tradition of studies investigates how networks of individual researchers or teams influence their success and creativity. However, less attention has been paid to the question of how networks vary between fields. In this study, we investigate how network processes—such as preferential attachment, homophily, and selection, as well as influence based on similar intellectual orientations—vary across scientific fields for the first time. We introduce a theoretical framework that extends a network ecological perspective to the domain of science and test it by analyzing collaboration and citation networks among ~4.8 million authors and ~12.5 million research articles in over 800 scientific fields derived from the PubMed knowledge graph and OpenAlex. We use relational hyper-event models (RHEMs) to study which contextual moderators—e.g., size, demographic composition, funding type and volume—foster certain network processes. For instance, our analysis allows us to investigate whether preferential attachment in collaboration and citation networks is stronger in fields that rely more on external funding or whether fields with an unbalanced gender composition show more gender homophily in collaborations. Our study makes two primary contributions: first, it provides the first large-scale analysis of network processes in biomedical research; second, it examines how these processes vary across fields. Our results are of interest to scientists and policymakers alike, as they allow us to map which contextual characteristics contribute to detrimental network processes, such as strong gender homophily or an excessive accumulation of resources by scientific elites.

In this comprehensive study, a group of researchers evaluated the reporting practices of relational data across 363 articles published in the REDES journal from 2002 to 2023. The approach involved a meticulous analysis of keywords, themes, and network metrics documented in these articles, followed by an assessment against 18 established reporting recommendations. This method allowed us to identify best practices and potential areas for standardization enhancement in future publications.

The findings reveal a broad range of network metrics and significant diversity in reporting styles. These variations underscore a critical challenge in ensuring comparability and replicability across studies, highlighting the pressing need for standardized reporting formats. A detailed examination shows inconsistent implementation of existing guidelines, pointing towards opportunities for refining these standards to improve their practical application.

The results emphasize the necessity of clear and coherent guidelines that aid in peer review processes, help train emerging researchers, and enhance consistency in scholarly outputs. The proposed recommendations advocate for precise definitions of network boundaries, thorough descriptions of data collection methods, and clear operationalization of network links. Adopting these practices will enhance the transparency and quality of network research, ultimately supporting more robust, replicable, and theoretically sound studies.

The researchers advocate for ongoing revisions of these guidelines to keep pace with methodological advancements in network analysis and evolving academic needs.

The growing complexity of networks has driven the development of various methodological approaches to uncover structural patterns, with applications across multiple fields. In the context of scientific collaboration, these methods offer valuable insights into the pathways of knowledge transfer.

This study introduces a novel approach for analyzing scientific collaboration by applying community detection and core-periphery analysis to a co-authorship network, where nodes represent authors and edges represent co-authored publications. Our analysis examines the collaboration patterns among Italian academic scholars from three distinct fields (statistics, sociology, and business) over a ten-year period (2012–2022). The study utilizes publication data sourced from Scopus and the Ministero dell'Istruzione e del Merito (MUR).

Unlike traditional core-periphery detection methods that focus on the classification of individual actors, our approach identifies core and peripheral communities within the network. To achieve this, we develop an innovative methodological framework building on a stochastic block model and optimizing an objective function that incorporates both inter-community connection density and strength. We further examine how author-specific characteristics, i.e. the academic field, influence community formation.

Our findings provide a foundation for assessing collaborative trends, knowledge diffusion, and the role of cohesive subgroups, contributing to a broader understanding of research networks.

An ambiguous idea rarely remains confined to a specific domain. Some, like free speech, values, and diversity are applied to broad swaths of society, becoming relevant to a wide array of citizens in many different contexts. The wide application of ambiguous ideas creates different emergent and evolutionary pathways for their development and use, resulting in a proliferation of literature and competing or even contradictory terminology. Developing insight into such ideas that diverge or coalesce within a community requires tools capable of untangling their lineage across a wide array of academic disciplines over several decades.

We will present three innovative applications of Social Network Analysis (SNA) methods to explore the emergence and evolution of accountability in higher education across a collection of 450 peer-reviewed articles published from 1974-2017 and their corresponding 12,270 references. First, we integrate qualitative data from articles and references into new interactive joint displays called Narrated Network Diagrams, creating opportunities to more accurately assess themes and meanings in literature by connecting structures in co-citation networks with relevant relational stories. Second, we elevate time in the analysis procedure to capture the dynamism of knowledge formation. Third, underutilized descriptive network statistics are applied to the co-citation network analysis to generate new insights such as different mechanisms for authors gaining influence in a knowledge community. Ultimately, we will present an innovative longitudinal Mixed Methods Social Network Analysis (MMSNA) approach to systematic literature reviews, significantly advancing previous SNA methods integration in this critical research practice.