The history of social network research is fundamentally linked to the methodological challenge of assessing people’s social relationships. Despite their longstanding use, name generators show considerable variation in their formulation, operationalisation and contextual embedding. The selection and formulation of name generators is typically informed by prior research and guided by consideration of the specific relational context under investigation. To support researchers in navigating these methodological choices and synthesising existing research, this project aims to develop a meta-analytic pipeline that extracts, synthesises and categorises methodological approaches used in previous studies. Using large language models (LLMs), the pipeline will automate the identification and extraction of methodological information from research articles, facilitating the aggregation of methodological choices and their corresponding empirical findings. The project will involve the development, implementation and validation of the pipeline by assessing its performance through manual human coding. The extracted methodological information includes key dimensions of the name generator (e.g. phrasing, relational context), sample characteristics and resulting network structures (e.g., average outdegree, density). By systematically organising and analysing these features, the pipeline will enable researchers to explore how name generators have been used across studies in a structured way. The overall goal is to provide a tool that offers comprehensive meta-analytic insights into methodological practices, allowing researchers to compare study designs, assess methodological trends, and evaluate the impact of specific name generators in different research contexts. This approach will increase methodological transparency, improve comparability across studies, and facilitate more informed decisions about design and implementation.

This study reviews and maps the diverse array of core-periphery detection algorithms employed in sociological and organizational research. Recognizing that these algorithms vary in assumptions, computational demands, and objectives, our work addresses the methodological challenges that scholars face when selecting an appropriate tool for network analysis.

We begin by cataloging the range of techniques that have been applied across disciplines, emphasizing their theoretical foundations and operational criteria. To further clarify the practical implications of these differences, we conduct a comparative analysis using simulated networks that represent both ideal core-periphery structures and networks with varying sizes, densities, and connectivity patterns. Our findings reveal that the performance of a given algorithm is closely tied to the specific characteristics of the network under study, thereby underscoring the need for a tailored methodological approach.

Additionally, we replicate and extend the influential study by Cattani and colleagues (American Sociological Review, 2014), revisiting its core-periphery perspective on cultural fields with updated techniques. This replication not only confirms previous insights but also highlights new dimensions in understanding social legitimacy and power distribution.

Ultimately, we offer practical guidelines for selecting the most plausible core-periphery detection algorithm, aligning methodological choices with research questions and data structure. This contribution aims to enhance the clarity, rigor, and effectiveness of social network analysis within the field of sociology.

Observing how social interactions in nature change over time and space is a major challenge. This study introduces the SocialScope project, which aims to develop an innovative approach to longitudinal data collection on social networks by integrating three technologies.

The first is a novel dual-radio proximity wearable sensor combining Bluetooth and ultra-wideband (UWB) radios, capturing the distance between individuals over time, therefore allowing the reconstruction of proximity networks from these spatial and temporal patterns [1].

The second is a smartphone application called iLog, which combines user self-reported information (e.g., time diary) and data passively collected from smartphone sensors [2]. Digital diaries provide repeated snapshots of participants' daily behaviours, minimizing reliance on retrospective recall and improving ecological validity. Smartphone sensors, e.g., GPS, allow us to track subjects' daily activities and social and spatial interactions. The third is a close-ended questionnaire.

Here we explore some early evidence on how people adjust interpersonal distances in everyday contexts based on gender, nationality, and age attributes. Eighteen college students living on the same dormitory floor wore the proximity sensors for 15 days, and continuously measured distances during real-life encounters every 15 seconds. At the same time, the app installed on the subjects' smartphones gathered time diary information every 30 minutes (Where are you? Who are you with? What are you doing? What is your mood?) and continuous information from the smartphone's sensors. The analysis is based on a total of 431,329 distances detected with the tag, with a total of 6,299 interactions lasting 90 seconds or longer, and 4,681 self-reported time diary information

When seeking to diffuse new information or encourage the adoption of a new behavior, it is common to recruit the assistance of Key Opinion Leaders (KOLs). Existing software (e.g. keyplayer) focuses on identifying the optimal KOL team of a given size. However, a series of practical challenges can arise in intervention settings that make this "optimal" KOL team infeasible. In this presentation, we review these practical challenges, then introduce and demonstrate the R package KOLpickeR, which facilitates the selection of KOL teams in the presence of common practical challenges.

The KOLpickeR package focuses on solving five practical challenges summarized by ABCDE:

* Availability - In some cases, certain individuals may be unavailable to serve as a KOL (e.g., declined, lack necessary skills), while others' membership on a KOL team may be necessary (e.g., they have already agreed). KOLpickeR allows the user to restrict the scope of potential KOL teams to those that include or exclude certain individuals.

* Breadth - The quality of a KOL team is typically evaluated based on the breadth of the other network members they can reach, but users often do not know which network metric is the most appropriate in a given context. KOLpickeR allows the user specify their goal (diffusion or adoption), then uses the most appropriate network metric(s).

* Cost - Larger KOL teams usually have wider breadth, but there is a cost associated with recruiting and training additional KOLs, so it is not always clear how many KOLs should be recruited. KOLpickeR allows the user to consider potential KOL teams with a range of sizes.

* Diversity - In some cases, it is important to ensure that members of the KOL team are diverse and representative of the larger network's population (e.g., to ensure buy-in). KOLpickeR allows the user to provide a categorical attribute, then computes each potential KOL team's diversity with respect to this attribute.

* Evaluation - When multiple possible KOL teams exist, the choice of a particular team requires balancing cost, breadth, and diversity. KOLpickeR computes and ranks a summary metric that simultaneously integrates these characteristics.

In addition to aiding in the identification and selection of KOL teams given the constraints that arise under practical challenges, KOLpickeR also generates network visualizations of KOL teams and their network coverage. These visualizations aid users in evaluating potential KOL teams, and in communicating the chosen KOL team to both researchers and intervention site partners.

While there are many computational network analytic tools available in R, most packages concentrate on particular types of networks, particular analyses or models, or particular applications. This fragmentation frustrates learning, teaching, exploration, innovation, and replication. The mission of manynet is to facilitate frictionless analysis of many networks for all. The purpose of this talk is to give an overview of manynet and related stocnet packages, and introduce the many ways they can be used to make and modify, intuitively map (visualize) and mark, identify memberships and motifs in, measure and model many types of networks. Examples are made available in the package through tutorials, complete with glossary entries, and a variety of exemplar data. Special attention in the presentation will be paid to recent developments and connections to recent models and standards.