arXiv subjects

Beatriz Arregui-García

Publications and source records attributed to Beatriz Arregui-García.

2 recordsLinked to original sources

arXiv2026

On the Effects of Decentralized Moderation on Network Robustness and Information Diffusion in Mastodon

Decentralized online social networks such as Mastodon distribute moderation power across thousands of independently governed servers, raising fundamental questions about how local block decisions shape global structure and information flow. In this paper, we analyze Mastodon at the instance level by constructing a signed, directed, temporal network in which positive edges aggregate inter-instance follow relationships and negative edges encode daily block actions. Using one year of data, we show that despite continuous moderation activity and changing roles among instances, the network exhibits strong structural stability: signed dyadic motifs and degree distributions display highly persistent dynamics, and aggregated transition matrices satisfy Markovian equilibrium conditions over intermediate time scales. Building on the marked asymmetry between instances that predominantly issue bans and those that are mostly banned, we then study information diffusion on the positive network via a hybrid contagion model that combines simple contagion within groups and complex contagion across groups. We find that information originating in the minority of moderating instances spreads more efficiently, both internally and toward the majority, while the opposite direction is fragile and sensitive to contagion parameters. Echo-chamber effects emerge even in a globally balanced signed network and become stronger under stricter contagion conditions. Together, these results show that decentralized moderation in Mastodon generates a stable macroscopic configuration that both structures and constrains information exchange, effectively isolating norm-violating domains without centralized control.

physics.soc-ph↗

arXiv2024

Patterns in temporal networks with higher-order egocentric structures

The analysis of complex and time-evolving interactions like social dynamics represents a current challenge for the science of complex systems. Temporal networks stand as a suitable tool to schematise such systems, encoding all the appearing interactions between pairs of individuals in discrete time. Over the years, network science has developed many measures to analyse and compare temporal networks. Some of them imply a decomposition of the network into small pieces of interactions, i.e. only involving a few nodes for a short time range. Along this line, a possible way to decompose a network is to assume an egocentric perspective, i.e. to consider for each node the time evolution of its neighbourhood. This has been proposed by Longa et al. by defining the "egocentric temporal neighbourhood", which has proven a useful tool to characterise temporal networks relative to social interactions. However, this definition neglects group interactions (quite common in social domains) as they are always decomposed into pairwise connections.A more general framework that allows us to consider also larger interactions is represented by higher-order networks. Here, we generalise the description of social interactions by making use of hypergraphs, consequently, we generalise its decomposition into "hyper egocentric temporal neighbourhoods". This will allow us to analyse social interactions, to compare different datasets or different nodes inside a dataset, by taking into account the intrinsic complexity represented by higher-order interactions. Even if we limit the order of interactions to the second (triplets of nodes), our results reveal the importance of a higher-order representation. In fact, our analyses show second-order structures are responsible for the majority of the variability at all scales: between datasets, amongst nodes and over time.

physics.soc-ph↗