Finding events in temporal networks: Segmentation meets densest-subgraph discovery

International audienceIn this paper we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event-discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time complexity. We adapt existing recent work on dynamic densest-subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard even for static graphs. However, on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extended this greedy approach for the case of temporal networks. However, the approximation guarantee does not hold. Nevertheless, according to the experiments, the algorithm finds good quality solutions

Finding events in temporal networks : segmentation meets densest subgraph discovery

In this paper, we study the problem of discovering a timeline of events in a temporal network. We model events as dense subgraphs that occur within intervals of network activity. We formulate the event discovery task as an optimization problem, where we search for a partition of the network timeline into k non-overlapping intervals, such that the intervals span subgraphs with maximum total density. The output is a sequence of dense subgraphs along with corresponding time intervals, capturing the most interesting events during the network lifetime. A naïve solution to our optimization problem has polynomial but prohibitively high running time. We adapt existing recent work on dynamic densest subgraph discovery and approximate dynamic programming to design a fast approximation algorithm. Next, to ensure richer structure, we adjust the problem formulation to encourage coverage of a larger set of nodes. This problem is NP-hard; however, we show that on static graphs a simple greedy algorithm leads to approximate solution due to submodularity. We extend this greedy approach for temporal networks, but we lose the approximation guarantee in the process. Finally, we demonstrate empirically that our algorithms recover solutions with good quality.Peer reviewe

Optimizing IGP Link Costs for Improving IP-level Resilience

Recently, major vendors have introduced new router platforms to the market that support fast IP-level failure pro- tection out of the box. The implementations are based on the IP Fast ReRoute–Loop Free Alternates (LFA) standard. LFA is simple, unobtrusive, and easily deployable. This simplicity, however, comes at a severe price, in that LFA usually cannot protect all possible failure scenarios. In this paper, we give new graph theoretical tools for analyzing LFA failure case coverage and we seek ways for improvement. In particular, we investigate how to optimize IGP link costs to maximize the number of protected failure scenarios, we show that this problem is NP- complete even in a very restricted formulation, and we give exact and approximate algorithms to solve it. Our simulation studies show that a deliberate selection of IGP costs can bring many networks close to complete LFA-based protection

Dynamic FOV visible light communications receiver for dense optical networks

This study explores how the field-of-view (FOV) of a visible light communications (VLCs) receiver can be manipulated to realise the best signal-to-noise ratio (SNR) while supporting device mobility and optimal access point (AP) selection. The authors propose a dynamic FOV receiver that changes its aperture according to receiver velocity, location, and device orientation. The D-FOV technique is evaluated through modelling, analysis, and experimentation in an indoor environment comprised of 15 VLC APs. The proposed approach is also realised as an algorithm that is studied through analysis and simulation. The results of the study indicate the efficacy of the approach including a 3X increase in predicted SNR over static FOV approaches based on measured received signal strength in the testbed. Additionally, the collected data reveal that D-FOV increases effectiveness in the presence of noise. Finally, the study describes the tradeoffs among the number of VLC sources, FOV, user device velocity, and SNR as a performance metric.Accepted manuscrip

Implementation and evaluation of a simulation system based on particle swarm optimisation for node placement problem in wireless mesh networks

With the fast development of wireless technologies, wireless mesh networks (WMNs) are becoming an important networking infrastructure due to their low cost and increased high speed wireless internet connectivity. This paper implements a simulation system based on particle swarm optimisation (PSO) in order to solve the problem of mesh router placement in WMNs. Four replacement methods of mesh routers are considered: constriction method (CM), random inertia weight method (RIWM), linearly decreasing Vmax method (LDVM) and linearly decreasing inertia weight method (LDIWM). Simulation results are provided, showing that the CM converges very fast, but has the worst performance among the methods. The considered performance metrics are the size of giant component (SGC) and the number of covered mesh clients (NCMC). The RIWM converges fast and the performance is good. The LDIWM is a combination of RIWM and LDVM. The LDVM converges after 170 number of phases but has a good performance.Peer ReviewedPostprint (author's final draft

PURIFY: a new approach to radio-interferometric imaging

In a recent article series, the authors have promoted convex optimization algorithms for radio-interferometric imaging in the framework of compressed sensing, which leverages sparsity regularization priors for the associated inverse problem and defines a minimization problem for image reconstruction. This approach was shown, in theory and through simulations in a simple discrete visibility setting, to have the potential to outperform significantly CLEAN and its evolutions. In this work, we leverage the versatility of convex optimization in solving minimization problems to both handle realistic continuous visibilities and offer a highly parallelizable structure paving the way to significant acceleration of the reconstruction and high-dimensional data scalability. The new algorithmic structure promoted relies on the simultaneous-direction method of multipliers (SDMM), and contrasts with the current major-minor cycle structure of CLEAN and its evolutions, which in particular cannot handle the state-of-the-art minimization problems under consideration where neither the regularization term nor the data term are differentiable functions. We release a beta version of an SDMM-based imaging software written in C and dubbed PURIFY (http://basp-group.github.io/purify/) that handles various sparsity priors, including our recent average sparsity approach SARA. We evaluate the performance of different priors through simulations in the continuous visibility setting, confirming the superiority of SARA