Graphs provide a natural data representation for analyzing the relationships among entities in many application areas. Since the analysis algorithms perform memory intensive operations, it is important that the graph layout is adapted to take advantage of the memory hierarchy. Here, we propose layout strategies based on community detection to improve the in-memory data locality of generic graph algorithms. We conclude that the detection of communities in a graph provides a layout strategy that improves the performance of graph algorithms consistently over other state of the art strategies. © 2011 Springer-Verlag.
CITATION STYLE
Prat-Pérez, A., Dominguez-Sal, D., & Larriba-Pey, J. L. (2011). Social based layouts for the increase of locality in graph operations. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6587 LNCS, pp. 558–569). https://doi.org/10.1007/978-3-642-20149-3_40
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