We introduce the computational problem of graphlet transform of a sparse large graph. Graphlets are fundamental topology elements of all graphs/networks. They can be used as coding elements for encoding graph-topological information at multiple granularity levels on a graph, and for making differentiation and connection across different graphs and networks. Network/graph analysis using multiple graphlets has growing applications. We recognize the universality and increased encoding capacity in using multiple graphlets, we address the arising computational complexity issues, and we present a fast method for exact graphlet transform. The fast graphlet transform establishes a few remarkable records at once in high computational efficiency, low memory consumption, and ready translation to high-performance program and implementation. It is intended to facilitate or enable advanced network/graph analysis with graphlets, and to advance at the same time sparse graph theory, high-performance graph computation  and broader applications.
We introduce the computational problem of graphlet transform of a sparse large graph. Graphlets are fundamental topology elements of all graphs/networks. They can be used as coding elements to encode graph-topological information at multiple granularity levels for classifying vertices on the same graph/network as well as for making differentiation or connection across different networks. Network/graph analysis using graphlets has growing applications. We recognize the universality and increased encoding capacity in using multiple graphlets, we address the arising computational complexity issues, and we present a fast method for exact graphlet transform. The fast graphlet transform establishes a few remarkable records at once in high computational efficiency, low memory consumption, and ready translation to high-performance program and implementation. It is intended to enable and advance network/graph analysis with graphlets, and to introduce the relatively new analysis apparatus to graph theory, high-performance graph computation , and broader applications.
R
for encoding
to encode
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on a graph, and
for classifying vertices on the same graph/network as well as
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and
or
D
graphs and
D
multiple
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facilitate or enable advanced
enable and advance
R
advance at the same time sparse
introduce the relatively new analysis apparatus to
A
,