Search

Community evolution remains a heavily researched and challenging area in the analysis of dynamic complex network structures. Currently, the primary limitation of traditional event-based approaches for community evolution analysis is the lack of strict constraint conditions for distinguishing evolutionary events, which entails that as the cardinality of discovered events increases, so does the number of redundant events. Another limitation of existing approaches is the lack of consideration for weak events. Weak events can be generated by small changes in communities, which are empirically prevalent, and are typically not captured by traditional events. To manage these two aforementioned limitations, this research aims to formalize a weak and strong events-based framework, which includes the following newly discovered events: “weak shrink,” “weak expand,” “weak merge,” and “weak splity” predicated on the community overlapping degree and community degree membership, this article refines these traditional strong events, as well as new constraints for weak events. In addition, a community evolution mining framework, which is based on both strong and weak events, is proposed and denoted by a weak-event-based community evolution method (WECEM). The framework can be summarized by the following: 1) communities in complex networks with adjacent time-stamps are compared to determine the community overlapping degree and community membership degree; 2) the values of the community overlapping degree and membership degree meet the definition of events; and 3) weak events are effectively identified. Extensive experimental results, on real and synthetic data sets consisting of dynamic complex networks and online social networks, demonstrate that WECEM is able to identify weak events more effectively than traditional frameworks. Specifically, WECEM outperforms traditional frameworks by 22.9% in the number of discovered strong events. The detection accuracy of evolutionary events is approximately 12.2% higher than that of traditional event-based frameworks. It is also worth noting that, as the cardinality of the data grows, the proposed framework, when compared with traditional frameworks, can more effectively, and efficiently, mine large-scale complex networks. [ABSTRACT FROM AUTHOR]

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy. [ABSTRACT FROM AUTHOR]

Community discovery plays an essential role in the analysis of the structural features of complex networks. Since online networks grow increasingly large and complex over time, the methods traditionally used for community discovery cannot efficiently handle large-scale network data. This introduces the important problem of how to effectively and efficiently discover large communities from complex networks. In this study, we propose a fast parallel community discovery model called picaso (a parallel community discovery a lgorithm based on approximate optimization), which integrates two new techniques: (1) Mountain model, which works by utilizing graph theory to approximate the selection of nodes needed for merging, and (2) Landslide algorithm, which is used to update the modularity increment based on the approximated optimization. In addition, the GraphX distribution computing framework is employed in order to achieve parallel community detection over complex networks. In the proposed model, clustering on modularity is used to initialize the Mountain model as well as to compute the weight of each edge in the networks. The relationships among the communities are then simplified by applying the Landslide algorithm, which allows us to obtain the community structures of the complex networks. Extensive experiments were conducted on real and synthetic complex network datasets, and the results demonstrate that the proposed algorithm can outperform the state of the art methods, in effectiveness and efficiency, when working to solve the problem of community detection. Moreover, we demonstratively prove that overall time performance approximates to four times faster than similar approaches. Effectively our results suggest a new paradigm for large-scale community discovery of complex networks. [ABSTRACT FROM AUTHOR]

A novel ridge-loaded staggered single-slot rectangular coupled-cavity traveling-wave tube (CC-TWT) operating at $X$ -band is presented. It features large interaction impedance and good thermal dissipation with a moderate bandwidth. Compared with the existing staggered single-slot rectangular CC-TWT and Hughes-type TWT, this structure has the advantage of large coupled impedance and high electron efficiency, which makes it potentially useful for miniaturization in CC-TWT. The electromagnetic characteristics and the beam–wave interaction based on this slow-wave structure are investigated. The calculation results predict that it potentially could provide saturated output power over 20 kW from 8.5 to 9.8 GHz when the cathode voltage is set from 25.4 to 26.4 kV and the beam current is 5 A, respectively. The corresponding saturated gain and the electron efficiency can reach over 34.4 dB and 15.2%. From 8.6 to 9.5 GHz, the gain is even over 38.6 dB, and the electron efficiency is more than 16.9%. The cold test results show that the voltage standing wave ratio of the high-frequency structure is below 2 at the range of 8.5–10 GHz. [ABSTRACT FROM PUBLISHER]

The problem of mobile sequential recommendation is to suggest a route connecting a set of pick-up points for a taxi driver so that he/she is more likely to get passengers with less travel cost. Essentially, a key challenge of this problem is its high computational complexity. In this paper, we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem consisting of two separate stages: an offline pre-processing stage and an online search stage. The offline stage pre-computes potential candidate sequences from a set of pick-up points. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Simultaneously, an incremental pruning policy is adopted in the process of sequence generation to reduce the search space of the potential sequences effectively. In addition, a batch pruning algorithm is further applied to the generated potential sequences to remove some non-optimal sequences of a given length. Since the pruning effectiveness keeps growing with the increase of the sequence length, at the online stage, our method can efficiently find the optimal driving route for an unloaded taxi in the remaining candidate sequences. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications. [ABSTRACT FROM PUBLISHER]

Clustering is an important technique for mining the intrinsic community structures in networks. The density-based network clustering method is able to not only detect communities of arbitrary size and shape, but also identify hubs and outliers. However, it requires manual parameter specification to define clusters, and is sensitive to the parameter of density threshold which is difficult to determine. Furthermore, many real-world networks exhibit a hierarchical structure with communities embedded within other communities. Therefore, the clustering result of a global parameter setting cannot always describe the intrinsic clustering structure accurately. In this paper, we introduce a novel density-based network clustering method, called graph-skeleton-based clustering (gSkeletonClu). By projecting an undirected network to its core-connected maximal spanning tree, the clustering problem can be converted to detect core connectivity components on the tree. The density-based clustering of a specific parameter setting and the hierarchical clustering structure both can be efficiently extracted from the tree. Moreover, it provides a convenient way to automatically select the parameter and to achieve the meaningful cluster tree in a network. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu for effective and efficient density-based clustering. [ABSTRACT FROM AUTHOR]