The increasing availability of dynamically growing digital data that can be used for extracting social networks has led to an upsurge of interest in the analysis of dynamic social networks. One key aspect of social network analysis is to understand the central nodes in a network. However, dynamic calculation of centrality values for rapidly growing networks might be unfeasibly expensive, especially if it involves recalculation from scratch for each time period. This paper proposes an incremental algorithm that effectively updates betweenness centralities of nodes in dynamic social networks while avoiding re-computations by exploiting information from earlier computations. Our performance results suggest that our incremental betweenness algorithm can achieve substantial performance speedup, on the order of thousands of times, over the state of the art, including the best-performing nonincremental betweenness algorithm and a recently proposed betweenness update algorithm.

Automation of data collection using online resources has led to significant changes in traditional practices of social network analysis. Social network analysis has been an active research field for many decades; however, most of the early work employed very small datasets. In this paper, a number of issues with traditional practices of social network analysis in the context of dynamic, large-scale social networks are pointed out. Given the continuously evolving nature of modern online social networking, we postulate that social network analysis solutions based on incremental algorithms will become more important to address high computation times for large, streaming, over-time datasets. Incremental algorithms can benefit from early pruning by updating the affected parts only when an incremental update is made in the network. This paper provides an example of this case by demonstrating the design of an incremental closeness centrality algorithm that supports efficient computation of all-pairs of shortest paths and closeness centrality in dynamic social networks that are continuously updated by addition, removal, and modification of nodes and edges. Our results obtained on various synthetic and real-life datasets provide significant speedups over the most commonly used method of computing closeness centrality, suggesting that incremental algorithm design is a fruitful research area for social network analysts.

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This paper identifies non-stationary effects in grid like Network-on-Chip (NoC) traffic and proposes QuaLe, a novel statistical physics-inspired model, that can account for non-stationarity observed in packet arrival processes. Using a wide set of real application traces, we demonstrate the need for a multi-fractal approach and analyze various packet arrival properties accordingly. As a case study, we show the benefits of our multifractal approach in estimating the probability of missing deadlines in packet scheduling for chip multiprocessors (CMPs).

Cataloged from PDF version of article.IEEE 802.16 standard (also known as WiMAX)\ud defines the wireless broadband network technology which aims\ud to solve the so called last mile problem via providing high\ud bandwidth Internet even to the rural areas for which the cable\ud deployment is very costly. The standard mainly focuses on the\ud MAC and PHY layer issues, supporting two transmission modes:\ud PMP (Point-to-Multipoint) and mesh modes. Mesh mode is an\ud optional mode developed as an extension to PMP mode and it\ud has the advantage of having an improving performance as more\ud subscribers are added to the system using multi-hop routes. In\ud 802.16 MAC protocol, mesh mode slot allocation and reservation\ud mechanisms are left open which makes this topic a hot research\ud area. Hence, the focus of this survey will mostly be on the mesh\ud mode, and the proposed scheduling algorithms and performance\ud evaluation methods