On the limits of DTN monitoring

Compared to wired networks, Delay/Disruption Tolerant Networks (DTN) are challenging to monitor due to their lack of infrastructure and the absence of end-to-end paths. This work studies the feasibility, limits and convergence of monitoring such DTNs. More specifically, we focus on the efficient monitoring of intercontact time distribution (ICT) between DTN participants. Our contribution is two-fold. First we propose two schemes to sample data using monitors deployed within the DTN. In particular, we sample and estimate the ICT distribution. Second, we evaluate this scheme over both simulated DTN networks and real DTN traces. Our initial results show that (i) there is a high correlation between the quality of sampling and the sampled mobility type, and (ii) the number and placement of monitors impact the estimation of the ICT distribution of the whole DTN

Towards modelling dialectic and eristic argumentation on the social web

Modelling arguments on the social web is a key challenge for those studying computational argumentation. This is because formal models of argumentation tend to assume dialectic and logical argument, whereas argumentation on the social web is highly eristic. In this paper we explore this gap by bringing together the Argument Interchange Format (AIF) and the Semantic Interlinked Online Communities (SIOC) project, and modelling a sample of social web arguments. This allows us to explore which eristic effects cannot be modelled, and also to see which features of the social web are missing.We show that even in our small sample, from YouTube, Twitter and Facebook, eristic effects (such as playing to the audience) were missing from the final model, and that key social features (such as likes and dislikes) were also not represented. This suggests that both eristic and social extensions need to be made to our models of argumentation in order to deal effectively with the social we

Faster Random Walks By Rewiring Online Social Networks On-The-Fly

Many online social networks feature restrictive web interfaces which only allow the query of a user's local neighborhood through the interface. To enable analytics over such an online social network through its restrictive web interface, many recent efforts reuse the existing Markov Chain Monte Carlo methods such as random walks to sample the social network and support analytics based on the samples. The problem with such an approach, however, is the large amount of queries often required (i.e., a long "mixing time") for a random walk to reach a desired (stationary) sampling distribution. In this paper, we consider a novel problem of enabling a faster random walk over online social networks by "rewiring" the social network on-the-fly. Specifically, we develop Modified TOpology (MTO)-Sampler which, by using only information exposed by the restrictive web interface, constructs a "virtual" overlay topology of the social network while performing a random walk, and ensures that the random walk follows the modified overlay topology rather than the original one. We show that MTO-Sampler not only provably enhances the efficiency of sampling, but also achieves significant savings on query cost over real-world online social networks such as Google Plus, Epinion etc.Comment: 15 pages, 14 figure, technical report for ICDE2013 paper. Appendix has all the theorems' proofs; ICDE'201

Missing data in multiplex networks: a preliminary study

A basic problem in the analysis of social networks is missing data. When a network model does not accurately capture all the actors or relationships in the social system under study, measures computed on the network and ultimately the final outcomes of the analysis can be severely distorted. For this reason, researchers in social network analysis have characterised the impact of different types of missing data on existing network measures. Recently a lot of attention has been devoted to the study of multiple-network systems, e.g., multiplex networks. In these systems missing data has an even more significant impact on the outcomes of the analyses. However, to the best of our knowledge, no study has focused on this problem yet. This work is a first step in the direction of understanding the impact of missing data in multiple networks. We first discuss the main reasons for missingness in these systems, then we explore the relation between various types of missing information and their effect on network properties. We provide initial experimental evidence based on both real and synthetic data.Comment: 7 page

Drawing Big Graphs using Spectral Sparsification

Spectral sparsification is a general technique developed by Spielman et al. to reduce the number of edges in a graph while retaining its structural properties. We investigate the use of spectral sparsification to produce good visual representations of big graphs. We evaluate spectral sparsification approaches on real-world and synthetic graphs. We show that spectral sparsifiers are more effective than random edge sampling. Our results lead to guidelines for using spectral sparsification in big graph visualization.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017