Aggregate relational data (ARD) on relationships between individuals and groups in a population can shed light on acquaintanceship network size, the level of segregation in contact with subpopulations, and the size of unlisted groups. Despite their wide range of applications, ARD questions can be cognitively demanding for respondents, as they require reporting the number of acquaintances in various subpopulations within the constrained time frames of typical survey settings. However, research on the susceptibility of ARD instruments to measurement error remains scarce. This study leverages the panel design of the Chilean Longitudinal Social Survey (ELSOC) to examine the reliability of individual ARD items and the network size measure obtained by combining them. To estimate reliability, I employ structural equation and multilevel modeling approaches commonly used in survey methodology research, adapting them to accommodate the unique structure of ARD. Individual items have reliability scores ranging from 0.41 to 0.51, depending on the reliability assessment method used. The reliability of the log-transformed network size is 0.59, indicating that combining the items reduces measurement error. Nevertheless, the reliability estimates of both individual items and the composite measure fall short of the commonly accepted 0.70 reliability threshold, raising concerns about bias and precision in ARD-based estimates of network size, segregation, and unlisted group sizes. These findings highlight the need for methodological refinements to improve the quality of ARD-derived data.

Feld and McGail (2020) explain how social networks distort perceptions of the social world. Highly connected individuals (high-degree nodes) are overrepresented in personal networks, leading to misperceptions where their traits are seen as more typical than is actually the case (Feld, 1991). The extent of this distortion depends on variation in individuals’ visibility, or indegree, which is difficult to assess when indegrees are unknown.

We propose a method using population surveys to estimate target indegree variance by counting how often the same targets are named by multiple informants. A high number of repeated mentions indicates high variation. We show how the ratio of repeated mentions to the square of total mentions provides a quantitative measure of the distorted experience of these targets.

We illustrate our method in academic citation networks, where readers may get inflated impressions of the value of academic articles in a journal, because the papers they find through citations are more highly cited than average for that journal. We measure this distortion in each journal by counting repeat citations in a sample of papers.

High variation in visibility among stigmatized individuals could lead to public perception being shaped by a few highly visible extroverts, while most others remain overlooked. Our method quantifies this variation in visibility. We illustrate its application and recognize challenges in implementation, addressing potential limitations.

This paper highlights the importance of measuring indegree variation to better understand distorted perceptions and provides a practical method for assessing distortions in many situations.

This work examines organised crime groups in Belgium involved in synthetic drug production and trafficking, ie. XTC and (meth)amphetamine. Belgium is a key European hub for large-scale production, with drugs trafficked globally. Organised crime groups largely control this market; however, limited knowledge exists about their structure and actors. Adopting a network perspective, this study uses Social Network Analysis (SNA) to identify key actors, or hubs and brokers, based on high centrality scores or social capital. Through a case file study in Flanders, the research reveals that high-social-capital actors were predominantly Belgian and Dutch males, many holding managerial roles in legal companies to launder profits. Hubs and brokers were closely connected, with brokers strategically positioned to link them. Disruption strategies targeting hubs and brokers yielded limited effects on this robust, scale-free network with small-world properties.

The purpose of the presented research is, through a case study of Operation Ethos, which directly impacted the First Command of the Capital - PCC, to demonstrate the need for a new look at criminal investigation, directing efforts to achieve with greater power and severity criminal agents that are defined as Topological High Return Targets. With the application of the Social Network Analysis, certain targets of greater influence and level of specialization within the criminal group would be subjected to more onerous sanctions – in particular with regard to the regime of serving sentences – as a means of discouraging (general and individual) and retribution for the gravity of its social impact. In the analysis presented, it was found that of the 54 (fifty-four) targets of Operation Ethos, three of them had a greater impact when removed from the structure of the criminal network, and, therefore, were the Targets of High Topological Return of that criminal cell. In this way, as they are the targets of greatest interest, they could also have been subjected to a differentiated sentence regime – a measure that is necessary in order to effectively impact organized crime.

The literature on whether the internet functions more as an echo chamber, reinforcing users' pre-existing views, or as a diverse forum presenting a multitude of perspectives is ongoing and marked by varied research outcomes. Some studies have identified a tendency for online spaces to foster ideological segregation, suggesting that digital platforms might indeed serve as echo chambers. Conversely, other research indicates that social media platforms, such as Twitter, could offer users exposure to a wider range of news sources and viewpoints than initially thought, challenging the notion of the internet solely as a space of ideological confinement.

Our research aims to deepen the understanding of echo chambers on Twitter/X, addressing gaps in prior studies that have primarily focused on the impact of ideology on information diversity while often overlooking crucial sociodemographic factors. Recognizing that many individuals do not engage deeply with political content, we emphasize the need to expand our inquiry beyond ideological divides. To this end, we employ a representative panel of 1.6 million Twitter/X accounts linked to voter files over four years (building on the approach proposed by Grinberg et al. 2019) and multiple waves of a large national survey (www.covidstates.org). This approach facilitates a comprehensive examination of information consumption and engagement patterns, highlighting the influence of gender, race, and rural living on public discourse and the variety of information accessed. Our objective is to move past the simplistic binary of ideological echo chambers, exploring a broader spectrum of user interactions.

We investigate three key aspects of information consumption on Twitter/X: the diversity of news consumption, the social network dynamics of news sharing, and the structure of user clusters around news sources. By examining whether users are exposed to diverse viewpoints or remain confined to echo chambers that reinforce existing beliefs, we seek to gauge the extent of diverse perspective encounters. Additionally, our analysis of how users cluster around certain news sources and how these clusters vary demographically is pivotal for determining whether Twitter/X serves as a platform for diverse idea exchange or as segmented spaces catering to specific group preferences.

This multifaceted investigation enables us to dissect the complex dynamics of echo chambers on Twitter/X, evaluating the platform's role in either facilitating a broad discourse or contributing to ideological segmentation. Our findings aim to illuminate the role of social media platforms in public discourse, opinion formation, and the vitality of democratic societies, highlighting Twitter's broader implications for societal dialogue and democracy.

In this paper I intend to understand Buddhism and the visions and practices of Buddhists (in new lands) using central aspects of Buddhism: non-self (non-duality) and dependent origination and interdependence

Relational sociology and "Social Network Analysis" offer us a new perspective where meaning is acquired in relationships, structures and systems of relationships. It is a vision closer to Buddhism: not-self, interdependence and causal interrelation, emptiness. Nothing makes sense on its own.

Relational sociology and "Social Network Analysis" take for granted the interaction, linkage, between everything that exists (such as Buddhism, and even quantum theories). It goes from the autonomous individual conceptual level to a higher level in abstraction with a meaning different from its components in interrelation. It is something new, socially, conceptually different. It is a new, different product and it is also changing.

Everything acquires meaning in interaction: different interactions imply/result in different meanings. Focusing on co-occurrences, coincidences, links or relationships allows us to approach relational systems and structures and to map/draw maps and social paths.

In this paper I apply the theoretical and technical approach of Social Network Analysis to understand the visions, values and practices of Buddhists as systems of interaction, breaking with duality. I use data from the first international survey of the Buddhist population in Spanish-speaking countries: "Following the Buddhist Path: Our Values, Religiosity, Spirituality" conducted in 2022.

The goal is to understand and visualize the complex system of interrelationships and interdependencies that shape our lives and the social and its meanings. To make Social Maps emerge along with the paths and connections between visions, meanings, actions and practices that make up the social map of a specific or global social space.