19 research outputs found

    Fast Computing Betweenness Centrality with Virtual Nodes on Large Sparse Networks

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    Betweenness centrality is an essential index for analysis of complex networks. However, the calculation of betweenness centrality is quite time-consuming and the fastest known algorithm uses time and space for weighted networks, where and are the number of nodes and edges in the network, respectively. By inserting virtual nodes into the weighted edges and transforming the shortest path problem into a breadth-first search (BFS) problem, we propose an algorithm that can compute the betweenness centrality in time for integer-weighted networks, where is the average weight of edges and is the average degree in the network. Considerable time can be saved with the proposed algorithm when , indicating that it is suitable for lightly weighted large sparse networks. A similar concept of virtual node transformation can be used to calculate other shortest path based indices such as closeness centrality, graph centrality, stress centrality, and so on. Numerical simulations on various randomly generated networks reveal that it is feasible to use the proposed algorithm in large network analysis

    Potential-based bounded-cost search and Anytime Non-Parametric A *

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    This paper presents two new search algorithms: Potential Search (PTS) and Anytime Potential Search/Anytime Non-Parametric A* (APTS/ANA*). Both algorithms are based on a new evaluation function that is easy to implement and does not require user-tuned parameters. PTS is designed to solve bounded-cost search problems, which are problems where the task is to find as fast as possible a solution under a given cost bound. APTS/ANA* is a non-parametric anytime search algorithm discovered independently by two research groups via two very different derivations. In this paper, co-authored by researchers from both groups, we present these derivations: as a sequence of calls to PTS and as a non-parametric greedy variant of Anytime Repairing A*. We describe experiments that evaluate the new algorithms in the 15-puzzle, KPP-COM, robot motion planning, gridworld navigation, and multiple sequence alignment search domains. Our results suggest that when compared with previous anytime algorithms, APTS/ANA*: (1) does not require user-set parameters, (2) finds an initial solution faster, (3) spends less time between solution improvements, (4) decreases the suboptimality bound of the current-best solution more gradually, and (5) converges faster to an optimal solution when reachable. © 2014 Elsevier B.V

    Routing betweenness centrality

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