There have been numerous recent attempts to “decentralize” social media platforms, loosely referred to as Web3. Such ideas, often underpinned by blockchain solutions, offer decentralized equivalents of well-known services (e.g., forums, social networks, video sharing sites, microblogs). One particularly challenging function to implement in such a design is content moderation, due to the lack of central control. Consequently, they often rely on user-controlled moderation, whereby each user must create their own personal block list to filter out content they do not wish to see. This paper presents a first study of user-controlled moderation on one exemplar Web3 social microblogging platform called memo.cash. Based on a dataset covering 391K posts, we study the factors that lead users to “mute” each other. We find that the most crucial factor is the platform action count, rather than the presence of things like hate speech. We also show that the followership network plays a pivotal role in determining their visibility on the platform, further influencing their muting behavior. This leads us to design tooling to automate the muting process on a per-user basis. We model this as a recommendation problem, and experiment with a number of state-of-the-art recommender engines. We show that our system can generate effective personalized mute lists for users.

There has been growing interest in the so-called “Web3” movement. This loosely refers to a mix of decentralized technologies, often underpinned by blockchain technologies. Among these, Web3 social media platforms have begun to emerge. These store all social interaction data (e.g., posts) on a public ledger, removing the need for centralized data ownership and management. But this comes at a cost, which some argue is prohibitively expensive. As an exemplar within this growing ecosytem, we explore memo.cash, a microblogging service built on the Bitcoin Cash (BCH) blockchain. We gather data for 24K users, 317K posts, 2.57M user actions, which have facilitated $6.75M worth of transactions. A particularly unique feature is that users must pay BCH tokens for each interaction (e.g., posting, following). We study how this may impact the social makeup of the platform. We therefore study memo.cash as both a social network and a transaction platform.

Gab is an online social network often associated with the alt-right political movement and users barred from other networks. It presents an interesting opportunity for research because near-complete data is available from day one of the network's creation. In this paper, we investigate the evolution of the user interaction graph, that is the graph where a link represents a user interacting with another user at a given time. We view this graph both at different times and at different timescales. The latter is achieved by using sliding windows on the graph which gives a novel perspective on social network data. The Gab network is relatively slowly growing over the period of months but subject to large bursts of arrivals over hours and days. We identify plausible events that are of interest to the Gab community associated with the most obvious such bursts. The network is characterised by interactions between 'strangers' rather than by reinforcing links between 'friends'. Gab usage follows the diurnal cycle of the predominantly US and Europe based users. At off-peak hours the Gab interaction network fragments into sub-networks with absolutely no interaction between them. A small group of users are highly influential across larger timescales, but a substantial number of users gain influence for short periods of time. Temporal analysis at different timescales gives new insights above and beyond what could be found on static graphs.

We propose a real-time adaptive- spatial sampling technique for the efficient collection of fine grained data in wireless sensor networks. The collection of fine grained data can incur high energy costs. This energy costs can be reduced by exploiting the spatial correlations of adjacent nodes, where only the most dominant nodes collect the data. We show that, using concepts developed in Random Matrix Theory, it is possible to determine the dominant nodes which enable to process noisy data in a time efficient, scalable, decentralized manner. The proposed technique has been validated using spatially interpolated pollution datasets giving good results in terms of data reduction and accuracy.

This paper shows that end to end traffic flows in a network that pass through disjoint paths affect each other. We refer to this as transitive relationship between traffic flows. We measure interdependencies among network elements by traffic correlation which leads to this transitive relationship between traffic flows. We show that the effect of transitive relationship becomes significant under certain congestion states in the network. This means that a problem might be present at a subset in the network but it's affect will be visible somewhere else. We also show that special care has to be taken while collecting flow data (for cross correlation) considering the delay distribution of traffic flows.

The authors review analysis, simulation and measurement techniques, the three fundamental methods for performance evaluation in packet networks, looking at what's known, what's new and some outstanding issues. In trying to avoid re-reviewing material which has already been well summarised elsewhere, the authors concentrate on areas that are relatively new or possibly less generally well appreciated. So, under analysis, the focus is on models for network topologies and connectivity, and on wireless access. In the simulation section the focus is on techniques for scalable simulation for large-scale packet networks. Compared to the other two areas measurement is relatively new anyway, and more time is spent on motivation, techniques and some recently discovered limitations.

When modeling the behavior of the Internet, it is vital to obtain a good description of its topology because structure fundamentally affects function. We introduce the positive-feedback preference (PFP) model, which is the most precise and complete Internet topology generator to date. The model uses two generic mechanisms based on observations on Internet history data, namely the interactive growth of new nodes and new internal links, combined with a novel nonlinear preferential probability, which is described by a positive-feedback mechanism. The PFP model accurately reproduces many topological properties of the AS-level Internet, including degree distribution, the maximum degree, rich-club connectivity, disassortative mixing, shortest-path length, short cycles and betweenness centrality. These properties are relevant to crucial network behaviors, such as routing efficiency, flexibility and traffic distribution. The PFP model can be used in more representative Internet simulations.

IP network technology is being increasingly used to support real-time telecommunications services. Network operators need to be able to dimension service partitions and virtual buffer sizes for efficient resource utilisation whilst meeting service level agreements, but without resorting to over-dimensioning. To provide the necessary quality of service guarantees for real time traffic, it is vital to understand the impact that self-similar behaviour can have on end to end delays. This paper introduces approximate analytical methods for assessing end to end delay variation bounds which yield accurate results over the full range of partition loadings, in contrast to other accepted approaches which are only valid for heavy traffic. Comparison of results against network simulations, in which local criticalities produce heavy-tailed delay distributions as a sub-component of the end to end performance, show good agreement. This demonstrates a key result, that the effect of self-similar behaviour on real-time service performance can be accurately assessed with easy-to-use analytical techniques incorporating Markovian assumptions.