This paper presents a measurement study of the topology and its effect on usage of Guifi.net, a large-scale community network. It focuses on the main issues faced by community network and lessons to consider for its future growth in order to preserve its scalability, stability and openness. The results show the network topology as an a typical high density Scale-Free network with critical points of failure and poor gateway selection or placement. In addition we have found paths with a large number of hops i.e. large diameter of the graph, and specifically long paths between leaf nodes and web proxies. The usage analysis using a widespread web proxy service confirms that these topological properties have an impact on the user experience.

This article examines the principles outlined in the General Data Protection Regulation in the context of social network data. We provide both a practical guide to General Data Protection Regulation--compliant social network data processing, covering aspects such as data collection, consent, anonymization, and data analysis, and a broader discussion of the problems emerging when the general principles on which the regulation is based are instantiated for this research area.

Community networks have emerged under the mottosCommunity networks have emerged under the mottos “break the strings that are limiting you”, “don't buy the network, be the network” or “a free net for everyone is possible”. Such networks create a measurable social impact as they provide to the community the right and opportunity of communication. As any other network that mixes wired and wireless links, the routing protocol must face several challenges that arise from the unreliable nature of the wireless medium. Link quality tracking helps the routing layer to select links that maximize the delivery rate and minimize traffic congestion. Moreover, link quality prediction has proved to be a technique that surpasses link quality tracking by foreseeing which links are more likely to change its quality. In this work, we focus on link quality prediction by means of a time series analysis. We apply this prediction technique in the routing layer of large-scale, distributed, and decentralized networks. We demonstrate that it is possible to accurately predict the link quality in 98% of the instances, both in the short and the long terms. Particularly, we analyse the behaviour of the links globally to identify the best prediction algorithm and metric, the impact of lag windows in the results, the prediction accuracy some time steps ahead into the future, the degradation of prediction over time, and the correlation of prediction with topological features. Moreover, we also analyse the behaviour of links individually to identify the variability of link quality prediction between links, and the variability of link quality prediction over time. Finally, we also present an optimized prediction method that considers the knowledge about the expected link quality values.Peer ReviewedPostprint (author's final draft

Three fundamental elements to understand human information networks are the individuals (actors) in the network, the information they exchange, that is often observable online as text content (emails, social media posts, etc.), and the time when these exchanges happen. An extremely large amount of research has addressed some of these aspects either in isolation or as combinations of two of them. There are also more and more works studying systems where all three elements are present, but typically using ad hoc models and algorithms that cannot be easily transfered to other contexts. To address this heterogeneity, in this article we present a simple, expressive and extensible model for temporal text networks, that we claim can be used as a common ground across different types of networks and analysis tasks, and we show how simple procedures to produce views of the model allow the direct application of analysis methods already developed in other domains, from traditional data mining to multilayer network mining.