class Partition[TLabel] extends AnyRef
The Partition class is used to partition large directed graphs.
It support the following three algorithms: 'group_ran', 'group_ord', 'group_lp'.
(1) Random Partitioning - excellent balance, poor edge cuts Each vertex is given a randomly assigned integer label 'ilabel' and is grouped accordingly.
(2) Ordered Partitioning - excellence balance, edge cuts may or may not be good Each vertex is assigned an integer label 'ilabel' incrementally and is grouped accordingly, e.g., {0, 1, ..., 9}, {10, 11, ..., 19}, ...
(3) Label Propagation partitioning - fair balance, fair edge cuts Each vertex is initially given a unique integer label 'ilabel'. On each iteration, each vertex will have its 'ilabel' reassigned to the most popular/frequent 'ilabel' in its neighborhood (which includes its children, parents and itself).
- See also
research.microsoft.com/pubs/183714/Partition.pdf ----------------------------------------------------------------------------
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- def formGraphs(): ArrayBuffer[Graph[TLabel]]
Form subgraphs 'gi's from the original graph 'g'
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- def group_lp(k: Int): Unit
Group the vertices based on their 'ilabel's using label propagation.
Group the vertices based on their 'ilabel's using label propagation.
- k
the number of subgraphs to create
- def group_ord(k: Int): Unit
Group the vertices based on the order of their 'ilabel's, which are assigned incrementally.
Group the vertices based on the order of their 'ilabel's, which are assigned incrementally.
- k
the number of subgraphs to create
- def group_ran(k: Int): Unit
Group the vertices based on their randomly generated 'ilabel's.
Group the vertices based on their randomly generated 'ilabel's.
- k
the number of subgraphs to create
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- def partition(): Unit
Partition vertices with the same 'ilabel' after grouping.
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