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Graph Manipulations for Fast Centrality Computation
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DOI：10.1145/3022668.png)
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摘要
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The betweenness and closeness metrics are widely used metrics in many network analysis applications. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework BADIOS that manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, the closeness computation time is decreased from more than 3 days to 6 hours (12.7x speedup).
Keyword:
Betweenness centrality
closeness centrality
shortest path
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An Efficient Algorithm for Nonlinear Model Predictive Control of Large-Scale Systems Part I: Description of the Method (Ein effizienter Algorithmus für die nichtlineare prädiktive Regelung großer Systeme Teil I: Methodenbeschreibung)大型系统非线性模型预测控制的有效算法第一部分: 方法的描述 (Ein effizienter algorithms f ü r die nichtlineare pr ä diktive Regelung gro ß er Systeme Teil I: Methodenbeschreibung).png)
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