Parallel PathFinder algorithms for mining structures from graphs

PathFinder networks are increasingly used in Data Mining for different purposes, like network visualization or knowledge extraction. This novel way of representing graphical data has been proven to give better results than other link reduction algorithms, like minimum spanning networks. However, this increase in quality comes with a high computation cost, typically of the order of n^̂3 or higher, where n is the number of nodes in the graph. While this problem has previously been tackled by using mathematical properties to speed up the algorithm, in this paper, we propose two new algorithms to speed up PathFinder computation based on parallelization techniques to take advantage of the increasingly available multi-core hardware platform. Experiments show that both new algorithms are more efficient than the state of the art algorithms; one of them can achieve speed-ups of up to x127 with an average of x23 on recent hardware (2007).