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.

We propose a method to detect communities in multi-relational networks, based on a graph neural network pipeline. The method allows to target areas where communities are consensual over the different modes of the network, which are processed as different networks in the pipeline. This is done by combining the outcomes of multiple simple Graph Neural Networks, applied on each of the graphs representing different forms of interactions between users of the social platform. The method is validated on a synthetic benchmark, as a first step for further improvements. In particular, the flexible architecture of the pipeline allows to swap its subparts and create variants of community detection.

Social network analysis research is typically done on real-world social network data. Together with a research paper, academics are confronted with the question of whether or not to publish the network data underlying their analyses. Here, a problem is that the structure of the network alone might disclose the identity of the nodes, i.e., the individual people present in the network. For example, if in a social network only one person has three friends of which two also know each other, then the ego network structure of this person reveals the person’s identity. Yet, to derive meaningful insights from this person’s social network, information about their ego network structure is inherently necessary. At first glance, the need for protecting people’s privacy is inherently at odds with ensuring reproducibility of social network analysis research. In this work, we discuss the network anonymization problem, which aims to achieve both objectives.

In the network anonymization problem, the goal is to perturb a given social network dataset in such a way that individuals can no longer be uniquely identified based on their surrounding network structure. We discuss how this problem relates to the more generic problem of statistical disclosure control, and cover a number of trade-offs that one needs to consider to address this problem in a social network scenario.

The first is that of the attacker scenario: what de-anonymizing information should we realistically assume is in the hands of an adversary, in case we are considering the structure of the social network as our main object of study? The second is the anonymity-utility-tradeoff: if we alter the network structure to increase anonymity, how can we still make sure that data utility is guaranteed? Utility can be measured in various ways, including preserving structural network properties and ensuring reliable performance on downstream tasks such as centrality analysis. The third is that of balancing computational complexity: depending on the chosen attacker scenario and corresponding measure of node anonymity, efficiently perturbing the network while retaining sufficient data utility can be a challenging computational problem.

We provide an overview of common network anonymity measures and network anonymization algorithms from the literature, and demonstrate a case study on the population-scale social network of the Netherlands, in which we show how various measures and algorithms perform in terms of attaining privacy and utility of the anonymized social network data. We furthermore demonstrate accompanying software that can anonymize a given social network dataset.

For nonzero vectors x and y, the Salton cosine index is defined as

S(x,y) = <x,y>/(|x|.|y|),

where <x,y> is the inner product and |x| = √<x,x>. It has the following properties: (1) S(x,y) ∈ [-1,1], (2) S(x,y) = S(y,x), (3) S(x,x) = 1, (4) x, y ≥ 0 ⇒ S(x,y) ∈ [0,1], (5) a, b ∈ ℝ ⇒ S(a.x,b.y) = S(x,y).

The Salton index measures similarity. It is usually transformed into a dissimilarity by d(x,y) = 1 - S(x,y) or d(x,y) = arccos(S(x,y))/π. The Salton index is especially useful in analyzing weighted networks because (property 5) it makes comparable nodes of different strengths.

Let W = [w[u,v]] be a matrix representation of a 2-mode network N = ((U, V), L, w); U, V are sets of nodes, L is the set of links, and w : L → ℝ is the weight. We can define a dissimilarity between nodes as D(u,v) = d(w[u,.],w[v,.]); w[u,.] is the matrix row of node u. The idea can not be directly applied to ordinary (1-mode, U = V) networks because in D(u,v) we would compare w[u,u] with w[u,v] and w[v,v] with w[v,u], but we must compare w[u,u] with w[v,v] and w[u,v] with w[v,u]. This is resolved by the corrected Salton index S'(u,v) = (<w[u,.],w[v,.]> + (w[u,u]-w[u,v]).(w[v,v]-w[v,u]))/(|w[u,.]|.|w[v,.]|). The properties 1-5 hold also for S'.

The Salton index can also be generalized to multiway networks, opening a way for new methods for their analysis. The details will be given in the presentation.

Examples from analyses of real-life networks will illustrate the proposed approaches. They are supported by an R package ClusNet available at https://github.com/bavla/Rnet/tree/master/R.

The Resilience Network (ResNet, see resnet.me) is a free mobile app together with several free web applications (surveys, scheduling polls, quizzes, etc.) designed to improve individual and group resilience by fostering social relationships and personal development. While primarily focused on improving the "7 life categories" of each app user, ResNet also offers many features for SNA and (health) intervention research.

ResNet collects data through an integrated psychological profiling questionnaire including the Big Five personality traits, through longitudinal sentiment tracking of all 7 life categories, and through interacting with others in the app. The collected data allows researchers to study sentiment dynamics, psychological profiles, and social behaviors. Health interventions such as promoting physical activity, improving nutrition, or leveraging positive outcomes from social interactions are within the scope of ResNet. Other types of interventions can be explored.

In this talk, we will demonstrate ResNet’s basic capabilities, including its web-based survey tool and its psychological profiling features. Using ResNet in research studies will be beneficial for the further development of the app. We especially welcome suggestions and collaborations for the use of ResNet in SNA and public health research.

Synthetic networks are an important tool for simulation studies of social phenomenon such as disease spread and behaviour adoption. Algorithms to generate such networks must be able to incorporate important features of real world social networks such as positive degree assortativity, clustering coefficient, and skewed degree distributions.

We have implemented a network generator that uses social proximity to drive edge creation. It adapts the algorithm of Pasta and colleagues (2014) that was designed to reproduce empirical Facebook friendship networks. Our implementation simplifies and generalises their algorithm, taking it beyond those specific networks.

In our paper, we present this implementation and preliminary results about the effects of algorithm parameters on the structural properties of the generated networks. These structural properties include mean and variation of centrality measures, network distances, clustering, and degree assortativity.

Collecting social network data, particularly complete networks and cognitive social structures, can be challenging. One challenge is that network surveys can be repetitive and tedious for respondents. While interfaces have been developed to make this process more engaging, they are typically limited to larger screens (e.g., laptops or tablets), whereas smartphones are more widely accessible. Another challenge is that complete network data regularly rely on the collection of names before obtaining informed consent, raising ethical concerns.

We introduce SICCEN (Smartphone Interface for Collecting Complete and Ego Networks), which was designed to collect quantitative social network data, including cognitive social structures, in an intuitive, engaging, and ethically responsible manner using smartphones. A key design feature of SICCEN is that connections are visualized by proximity rather than lines, ensuring clarity and scalability on small screens, even when capturing complex cognitive social structures. Another key feature is that when network members participate simultaneously, they can enter their names at the start of the questionnaire, which are then dynamically integrated into other participants’ surveys in real time. This method eliminates the need to collect personal data in advance and automatically excludes non-participants’ names, addressing ethical concerns.

We will discuss the background of SICCEN, its benefits for social network research, how it was developed, its novel features, how we used it for a large-scale school study (N = 1491), and directions for its future development.