Huanqing Cui

With the exponential growth of graph‐structured data, single‐machine efficient analysis has become increasingly impractical, making high‐performance distributed graph computing systems indispensable. The efficacy of these systems hinges critically on high‐quality graph partitioning. The edges of many graphs stemmed from real applications are uncertain, but many existing graph partitioning algorithms are only for deterministic graphs without considering uncertainty. This paper presents a novel partitioning algorithm, PAUG (Partitioning Algorithm for Uncertain Graphs), tailored for uncertain graphs. First, it formalizes the partitioning problem as an optimization task to minimize the cut‐edge ratio while balancing load. Second, it introduces probabilistic similarity to quantify vertex relationships under uncertainty. Finally, it details the PAUG algorithm which consists of initial partition phase and score‐function‐guided refinement strategy. Experimental results shows that PAUG achieves an average 23.2% reduction in cut‐edge ratio and a 26.2% improvement in load balance over state‐of‐the‐art algorithms.

As the backbone of the Internet of Things, wireless sensor networks are widely applied to perceive the physical world. Most applications need to associate perception information with a position to generate physical significance. This paper proposes WRCDV-Hop, which has quadruple improvements of the well-known DV-Hop. First, the hop count between a pair of sensor nodes is measured as a continuous value rather than a discrete value. Second, the unknown nodes calculate the average distance per hop by the weighted method. Third, each sensor node only records and relays limited but sufficient beacons of the anchors. Fourth, the unknown nodes apply the whale optimization algorithm to estimate positions. The first two improvements ensure that the distance estimation between a pair of sensor nodes is highly accurate, and the third improvement reduces the energy consumption. The last improvement makes the position estimation more precise. The simulation results show that WRCDV-Hop performs well in terms of localization accuracy and energy consumption.

Localization is crucial to wireless sensor networks. Among the recently proposed localization algorithms, the mobile anchor-assisted localization (MAL) algorithm seems promising. A MAL algorithm using a single mobile anchor has low energy consumption but a high localization error. Conversely, a MAL algorithm with three or more mobile anchors has minor localization errors but high energy consumption. By balancing energy consumption and localization accuracy, our study developed a localization algorithm assisted by two mobile anchors. A mobile anchor traverses the network along a double anchor SCAN (DASCAN) path, which divides the deployment region into grids and requires the two mobile anchors to traverse different horizontal lines in a zigzag pattern. Sensor nodes estimate their locations using a multiple-disturbance strategy grey wolf optimization (MDS-GWO) algorithm, which improves optimization by introducing a nonlinearly decreasing weight, a random perturbation of grey wolves and a mirror grey wolf. Using MATLAB, DASCAN was compared with GTURN, GSCAN, PP-MMAN, H-Curves, M-Curves, and SCAN paths by their energy consumption and localization rates. The localization error of MDS-GWO was compared with trilateration, PSO, WOA, and GWO. The impacts of radio irregularity, radio radius, and fading effect on MDS-GWO with different paths were also analyzed. The simulation results showed that the energy consumption of DASCAN was, on average, 30.1% less than GSCAN, GTURN, and PP-MMAN, but they had almost the same localization accuracy. The energy consumption of DASCAN was an average of 18.67% more than M-Curves, H-Curves, and SCAN, but the localization error of DASCAN was average of 32.3% less than SCAN, H-Curves, and M-Curves. The localization error of MDS-GWO was average of 25.5% less than trilateration, PSO, WOA, and GWO. Moreover, the performance of the proposed algorithm was less affected by different setups than the compared methods.

Debt is common in daily transactions, but it may bring great harm to individuals, enterprises, and society and even lead to a debt crisis. This paper proposes a weighted directed multi-arc graph model DebtG of debts among a large number of entities, including individuals, enterprises, banks, and governments, etc. Both vertices and arcs of DebtG have attributes. In further, it defines three basic debt structures: debt path, debt tree, and debt circuit, and it presents algorithms to detect them and basic methods to solve debt clearing problems using these structures. Because the data collection and computation need a third-party platform, this paper also presents the profit analysis of the platform. It carries out a case analysis using the real-life data of enterprises in Huangdao Zone. Finally, it points out four key problems that should be addressed in the future.

The multistage graph problem is a special kind of single-source single-sink shortest path problem. It is difficult even impossible to solve the large-scale multistage graphs using a single machine with sequential algorithms. There are many distributed graph computing systems that can solve this problem, but they are often designed for general large-scale graphs, which do not consider the special characteristics of multistage graphs. This paper proposes DMGA (Distributed Multistage Graph Algorithm) to solve the shortest path problem according to the structural characteristics of multistage graphs. The algorithm first allocates the graph to a set of computing nodes to store the vertices of the same stage to the same computing node. Next, DMGA calculates the shortest paths between any pair of starting and ending vertices within a partition by the classical dynamic programming algorithm. Finally, the global shortest path is calculated by subresults exchanging between computing nodes in an iterative method. Our experiments show that the proposed algorithm can effectively reduce the time to solve the shortest path of multistage graphs.

Graph is an important model to describe various networks, and its scale becomes larger and larger with the development of communication and information technology. The analysis of large-scale graphs requires distributed graph processing systems, and graph partition is the basis of these systems. The existing graph partitioning algorithms are almost proposed for homogeneous clusters, which don't consider the differences among computing nodes in heterogeneous clusters. This paper proposes GAP, a Genetic Algorithm based graph Partitioning algorithm to solve this problem. GAP aims to reduce the total processing time on a heterogeneous cluster by partitioning graphs according to the computing powers of computing nodes. GAP balanced partition the graph initially, and then utilizes genetic algorithm to transfer vertices to reduce cut edges. GAP can balance the processing time of computing nodes, and reduce the communication time among computing nodes. The experiments performed on a heterogeneous cluster demonstrate the outperformance of GAP than Hash.

To overcome the disadvantages of the traditional block-matching-based image denoising method, an image denoising method based on block matching with 4D filtering (BM4D) in the 3D shearlet transform domain and a generative adversarial network is proposed. Firstly, the contaminated images are decomposed to get the shearlet coefficients; then, an improved 3D block-matching algorithm is proposed in the hard threshold and wiener filtering stage to get the latent clean images; the final clean images can be obtained by training the latent clean images via a generative adversarial network (GAN).Taking the peak signal-to-noise ratio (PSNR), structural similarity (SSIM for short) of image, and edge-preserving index (EPI for short) as the evaluation criteria, experimental results demonstrate that the proposed method can not only effectively remove image noise in high noisy environment, but also effectively improve the visual effect of the images.