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Numerous discussions have advocated the presence of a so-called rabbit-hole (RH) phenomenon on social media, interested in advanced personalization to their users. This phenomenon is loosely understood as a collapse of mainstream recommendations, in favor of ultra-personalized ones that lock users into narrow and specialized feeds. Yet quantitative studies are often ignoring personalization, are of limited scale, and rely on manual tagging to track this collapse. This precludes a precise understanding of the phenomenon based on reproducible observations, and thus the continuous audits of platforms. In this paper, we first tackle the scale issue by proposing a user-sided bot-centric approach that enables large-scale data collection, through autoplay walks on recommendations. We then propose a simple theory that explains the appearance of these RHs. While this theory is a simplifying viewpoint on a complex and planet-wide phenomenon, it carries multiple advantages: it can be analytically modeled, and provides a general yet rigorous definition of RHs. We define them as an interplay between (i) user interaction with personalization and (ii) the attraction strength of certain video categories, which cause users to quickly step apart of mainstream recommendations made to fresh user profiles. We illustrate these concepts by highlighting some RHs found after collecting more than 16 million personalized recommendations on YouTube. A final validation step compares our automatically identified RHs against manually identified RHs from a previous research work. Together, those results pave the way for large-scale and automated audits of the RH effect in recommendation systems. [ABSTRACT FROM AUTHOR]

Powerline communication networks assume an interesting position in the communication network space: similarly to wireless networks, powerline networks are based on a shared broadcast medium; unlike wireless networks, however, the signal propagation is constrained to the power lines of the electrical infrastructure, which is essentially a graph. This article presents an algorithmic model to study the design of communication services over powerline communication networks. As a case study, we focus on the fundamental broadcast problem, and present and analyze a distributed algorithm $$\textsc {Color}\textsc {Cast}$$ which terminates in at most n communication rounds, where n denotes the network size, even in a model where link qualities are unpredictable and time-varying. For comparison, the achieved broadcast time is lower than what can be achieved by any unknown-topology algorithm (lower bounds $$\varOmega (n\log n / \log (n/D))$$ and $$\varOmega (n \log D)$$ are proved in Kowalski and Pelc (Distrib Comput 18(1):43-57, ) resp. Clementi et al. () where D is the network diameter). Moreover, existing known-topology broadcast algorithms often fail to deliver the broadcast message entirely in this model. This article also presents a general broadcast lower bound for the powerline model. [ABSTRACT FROM AUTHOR]

Network virtualization is a new Internet paradigm which allows multiple virtual networks (VNets) to share the resources of a given physical infrastructure. The virtualization of entire networks is the natural next step after the virtualization of nodes and links. While the problem of how to embed a VNet ('guest network') on a given resource network ('host network') is algorithmically well-understood, much less is known about the security implications of this new technology. This paper introduces a new model to reason about one particular security threat: the leakage of information about the physical infrastructure-often a business secret. We initiate the study of this new problem and introduce the notion of request complexity which describes the number of VNet requests needed to fully disclose the substrate topology. We derive lower bounds and present algorithms achieving an asymptotically optimal request complexity for important graph classes such as trees, cactus graphs (complexity $$O(n)$$ ) as well as arbitrary graphs (complexity $$O(n^2)$$ ). Moreover, a general motif-based topology discovery framework is described which exploits the poset structure of the VNet embedding relation. [ABSTRACT FROM AUTHOR]

Traceroute measurements are one of the main instruments to shed light onto the structure and properties of today's complex networks such as the Internet. This article studies the feasibility and infeasibility of inferring the network topology given traceroute data from a worst-case perspective, i.e., without any probabilistic assumptions on, e.g., the nodes' degree distribution. We attend to a scenario where some of the routers are anonymous, and propose two fundamental axioms that model two basic assumptions on the traceroute data: (1) each trace corresponds to a real path in the network, and (2) the routing paths are at most a factor $$1/\alpha $$ off the shortest paths, for some parameter $$\alpha \in (0,1]$$. In contrast to existing literature that focuses on the cardinality of the set of (often only minimal) inferrable topologies, we argue that a large number of possible topologies alone is often unproblematic, as long as the networks have a similar structure. We hence seek to characterize the set of topologies inferred with our axioms. We introduce the notion of star graphs whose colorings capture the differences among inferred topologies; it also allows us to construct inferred topologies explicitly. We find that in general, inferrable topologies can differ significantly in many important aspects, such as the nodes' distances or the number of triangles. These negative results are complemented by a discussion of a scenario where the trace set is best possible, i.e., 'complete'. It turns out that while some properties such as the node degrees are still hard to measure, a complete trace set can help to determine global properties such as the connectivity. [ABSTRACT FROM AUTHOR]