I am working on the lay outing algorithm, for this I need a data structure which should be able to store and access information about nodes, edges and clusters very efficiently.
Boost Subgraph structure handles at least data efficiently as per the research but anyone has idea how it is used and prepared? Please help me if any one has worked on such problem?
Hello,
Perhaps you can find some basic informations in the (very) old book (by Narsingh DEO) " Graph Theory with Applications to Engineering and Computer Science" ; Prentice-Hall Inc, 1974
Look at Chapter 11
Hi Mayur
What order of magnitude are you going to be working on? Thousands of elements? Millions?
Boost is linked to your code like any other library, you might want to have a look at: http://www.boost.org/doc/libs/1_39_0/more/getting_started/unix-variants.html
Other than this, you might want to have a look at Networkx, or iGraph (for Python) for rapid prototyping purposes, if you want to quickly test an algorithm (https://networkx.github.io/ and http://igraph.org/redirect.html).
I have used Networkx with datasets of a few hundred thousand nodes without any problems. Other than this, graphviz can be used to visualise graphs with millions of nodes but you are going to need a machine with a lot of memory (>6GB). Typical rendering times for such tasks might go well over 2hrs (http://www.graphviz.org/ and http://yifanhu.net/GALLERY/GRAPHS/index.html).
Hope this helps.
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