Tweet clustering for event detection is a powerful modern method to automate the real-time detection of events. In this work we present a new tweet clustering approach, using a probabilistic approach to incorporate temporal information. By analysing the distribution of time gaps between tweets we show that the gaps between pairs of related tweets exhibit exponential decay, whereas the gaps between unrelated tweets are approximately uniform. Guided by this insight, we use probabilistic arguments to estimate the likelihood that a pair of tweets are related, and build an improved clustering method. Our method Social Media Event Response Clustering (SMERC) creates clusters of tweets based on their tendency to be related to a single event. We evaluate our method at three levels: through traditional event prediction from tweet clustering, by measuring the improvement in quality of clusters created, and also comparing the clustering precision and recall with other methods. By applying SMERC to tweets collected during a number of sporting events, we demonstrate that incorporating temporal information leads to state of the art clustering performance.

Network topology synthesis seeks methods to generate large numbers of example network topologies primarily for use in simulation. It is a topic that has received much attention over the years, underlying which is a conflict between randomness and design. Random graphs are appealing because they are simple and avoid the messy details that plague real networks. However real networks are messy, because network operators design their networks in the context of complex technological constraints, costs, and goals. When random models have been used they often produce patently unrealistic networks that only match a few artificial connectivity statistics of real networks: the features that make the network useful and interesting are ignored. At best a network divorced from context is a purely mathematical object with no meaning or utility. At worst it can be completely misleading. However, design alone cannot generate an ensemble of networks with the variability needed in simulation. We need to balance design and randomness in a way that generates reasonable networks with given characteristics and predictable variability. This paper presents such a method, Combined Optimization and Layered Design (COLD), incorporating randomness and design principles to create ensembles of PoP-level synthetic networks.

No abstract available.

What does a childpsilas search of a large, complex cartoon for the eponymous character (Waldo) have to do with Internet routing? Network operators also search complex datasets, but Waldo is the least of their worries. Routing oscillation is a much greater concern. Networks can be designed to avoid routing oscillation, but the approaches so far proposed unnecessarily reduce the configuration flexibility. More importantly, apparently minor changes to a configuration can lead to instability. Verification of network stability is therefore an important task, but unlike the childpsilas search, this problem is NP hard. Until now, no practical method was available for large networks. In this paper, we present an efficient algorithm for proving stability of iBGP, or finding the potential oscillatory modes, and demonstrate its efficacy by applying it to the iBGP configuration of a large Tier-2 AS.

Wireless ad hoc networks provide a useful communications infrastructure for the mobile battlefield. In this paper we apply and develop passive radio frequency signal strength monitoring and packet transmission time profiling techniques, to characterise and reconstruct an encrypted wireless network's topology. We show that by using signal strength measurements from three or more wireless probes and by assuming the use of carrier sense multiple access with collision avoidance, for physical layer control, we can produce a representation of a wireless network's logical topology and in some cases reconstruct the physical topology. Smoothed Kalman filtering is used to track the reconstructed topology over time, and in conjunction with a weighted least squares template fitting technique, enables the profiling of the individual network nodes and the characterisation of their transmissions.