Abstract:A graph is a very important structure to describe many applications in the real world. In many applications, such as dependency graphs and debt graphs, it is an important problem to find and remove cycles to make these graphs be cycle-free. The common algorithm often leads to an out-of-memory exception in commodity personal computer, and it cannot leverage the advantage of multicore computers. This paper introduces a new problem, cycle detection and removal with vertex priority. It proposes a multithreading iterative algorithm to solve this problem for large-scale graphs on personal computers. The algorithm includes three main steps: simplification to decrease the scale of graph, calculation of strongly connected components, and cycle detection and removal according to a pre-defined priority in parallel. This algorithm avoids the out-of-memory exception by simplification and iteration, and it leverages the advantage of multicore computers by multithreading parallelism. Five different versions of the proposed algorithm are compared by experiments, and the results show that the parallel iterative algorithm outperforms the others, and simplification can effectively improve the algorithm's performance.
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.
Due to uneven deployment of anchor nodes in large-scale wireless sensor networks, localization performance is seriously affected by two problems. The first is that some unknown nodes lack enough noncollinear neighbouring anchors to localize themselves accurately. The second is that some unknown nodes have many neighbouring anchors to bring great computing burden during localization. This paper proposes a localization algorithm which combined niching particle swarm optimization and reliable reference node selection in order to solve these problems. For the first problem, the proposed algorithm selects the most reliable neighbouring localized nodes as the reference in localization and using niching idea to cope with localization ambiguity problem resulting from collinear anchors. For the second problem, the algorithm utilizes three criteria to choose a minimum set of reliable neighbouring anchors to localize an unknown node. Three criteria are given to choose reliable neighbouring anchors or localized nodes when localizing an unknown node, including distance, angle, and localization precision. The proposed algorithm has been compared with some existing range-based and distributed algorithms, and the results show that the proposed algorithm achieves higher localization accuracy with less time complexity than the current PSO-based localization algorithms and performs well for wireless sensor networks with coverage holes.
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