In this paper we present a multi-grained parallel algorithm for computing betweenness centrality, which is extensively used in large-scale network analysis. Our method is based on ...
This paper has no novel learning or statistics: it is concerned with making a wide class of preexisting statistics and learning algorithms computationally tractable when faced wit...
In this paper we work on the parallelization of the inherently serial Dijkstra's algorithm on modern multicore platforms. Dijkstra's algorithm is a greedy algorithm that ...
Skew is prevalent in data streams, and should be taken into account by algorithms that analyze the data. The problem of finding "biased quantiles"-- that is, approximate...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
A genetic algorithm is combined with two variants of the modularity (Q) network analysis metric to examine a substantial amount fisheries catch data. The data set produces one of t...
Garnett Carl Wilson, Simon Harding, Orland Hoeber,...