The design of graph algorithms plays a fundamental role to solve many of the computational problems that arise from several fields. The efficiency of an algorithm is a critical parameter that determinates its applicability, and it is becoming more and more critical nowadays when the amount of data and information is growing to sizes of terabytes (e.g. Internet search databases, geographical information systems, and bio informatics). For non-massive data sets, the efficiency directly depends on the number of elementary operations that the algorithm makes in main memory, but for massive data sets, the bottleneck of the computation is the number of times weave to probe (or access) the data through the memory hierarchy. In this work, the connection between information hidden in the data will be modelled in a graph-theoretical way giving us (massive) graphs that we want to deal with.
Efficient, new graph algorithms will be developed that will take into account the trade-off between time and space in massive and non-massive data sets. According to the leading expertise of the host, emphasis will be put on techniques based on graph minors and topological methods. The researcher will receive advanced training in the methods and techniques commonly used in graph and external memory algorithms. Together with his previous work on computational geometry, the researcher will have acquired broad knowledge of a cross-section of discrete algorithms.
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