Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute's HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Di Fatta, G., & Berthold, M. R. (2005). High performance subgraph mining in molecular compounds. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3726 LNCS, pp. 866–877). Springer Verlag. https://doi.org/10.1007/11557654_97
Mendeley helps you to discover research relevant for your work.