A modularity-specialized label propagation algorithm (LPAm) for detecting network communities was recently proposed. This promising algorithm offers some desirable qualities. However, LPAm favors community divisions where all communities are similar in total degree and thus it is prone to get stuck in poor local maxima in the modularity space. To escape local maxima, we employ a multistep greedy agglomerative algorithm (MSG) that can merge multiple pairs of communities at a time. Combining LPAm and MSG, we propose an advanced modularity-specialized label propagation algorithm (LPAm+). Experiments show that LPAm+ successfully detects communities with higher modularity values than ever reported in two commonly used real-world networks. Moreover, LPAm+ offers a fair compromise between accuracy and speed.

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SummaryCommunity detection in networks receives much attention recently. Most of the previous works are for unipartite networks composed of only one type of nodes. In real world situations, however, there are many bipartite networks composed of two types of nodes. In this paper, we propose a fast algorithm called LP&BRIM for community detection in large-scale bipartite networks. It is based on a joint strategy of two developed algorithms --label propagation (LP), a very fast community detection algorithm, and BRIM, an algorithm for generating better community structure by recursively inducing divisions between the two types of nodes in bipartite networks. Through experiments, we demonstrate that this new algorithm successfully finds meaningful community structures in large-scale bipartite networks in reasonable time limit.

Modularity evaluates the quality of a division of network nodes into communities, and modularity optimization is the most widely used class of methods for detecting communities in networks. In bipartite networks, there are correspondingly bipartite modularity and bipartite modularity optimization. LPAb, a very fast label propagation algorithm based on bipartite modularity optimization, tends to become stuck in poor local maxima, yielding suboptimal community divisions with low bipartite modularity. We therefore propose LPAb+, a hybrid algorithm combining modified LPAb, or LPAb’, and MSG, a multistep greedy agglomerative algorithm, with the objective of using MSG to drive LPAb out of local maxima. We use four commonly used real-world bipartite networks to demonstrate LPAb+ capability in detecting community divisions with remarkably higher bipartite modularity than LPAb. We show how LPAb+ outperforms other bipartite modularity optimization algorithms, without compromising speed.

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Recently emerged cloud computing offers a promising platform for executing scientific workflow applications due to its similar performance compared to the grid, lower cost, elasticity and so on. Collaborative cloud environments, which share resources of multiple geographically distributed data centers owned by different organizations enable researchers from all over the world to conduct their large scale data intensive research together through Internet. However, since scientific workflows consume and generate huge amount of data, it is thus essential to manage the data effectively for the purpose of high performance and cost effectiveness. In this paper, we propose intelligent data placement strategy to improve performance of workflows while minimizing data transfer among data centers. Specifically, at the startup stage, the whole dataset is divided into small data items which are then distributed among multiple data centers by considering these data centers' computation capability, storage budget, data item correlation, etc. During the runtime stage, when intermediate data is generated, it is placed on the suitable data centers using linear discriminant analysis by taking into account the same metrics as at the startup stage, as well as data centers' past behaviors (i.e., trustworthiness in terms of task delay). Simulation results demonstrate the promise of our data placement strategy by showing that compared to existing data placement strategies, our proposal effectively places the data to improve computation progress on the whole while minimizing the communication overheads incurred by data movement.

PDAs have evolved over the years from resource constrained devices that supported only the most basic tasks to powerful handheld computing devices. However, the most significant step in the evolution of PDAs was the introduction of wireless connectivity which enabled them to host applications that require internet connectivity like email, web browsers and maybe most importantly smart/rich clients. Being able to host smart clients allows the users of PDAs to seamlessly access the IT resources (e.g. legacy apps) of their organizations. One increasingly popular way of enabling access to IT resources is by using Web Services (WS) [14]. This trend has been aided by the rapid availability of Web Service (WS) packages/tools, most notably the efforts of the Apache group [1] and IDE vendors (e.g., Microsoft's Visual Studio [2], IBM's Eclipse [3]). Using IDE tools and other software packages it is fairly easy for programmers to expose application interfaces and/or consume existing interfaces leading to a gradual replacement of the current web server centric approaches (e.g. ASP, JSP, Servlets, CGI scripts) with WS centric approach. This paper focuses on the challenges of enabling PDAs to host Web Services consumers and introduces a dual caching approach to overcome problems arising from temporarily loss of connectivity and fluctuations in bandwidth.

The photovoltaic properties of YBa2Cu3O7−δ/Nb-doped SrTiO3 (SNTO) heterostructures were investigated systematically under laser irradiation of different wavelengths from 365 nm to 640 nm. A clear photovoltaic effect was observed, and the photovoltage Voc ranged from 0.1 V to 0.9 V depending on the wavelength. The Voc appeared under laser illumination with a photon energy of 2.4 eV, far below the band gap (3.2 eV) of Nb-doped SrTiO3. The temperature dependencies of the Voc and short-current density showed kinks near the structural phase transition of the Nb-doped SrTiO3. Our findings are helpful for understanding the photovoltaic effect in transition-metal oxide based heterojunctions and designing such photovoltaic devices.