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Graph structure is a very powerful tool to model system and represent their actual shape. For instance,
modelling an infrastructure or social network naturally leads to graph. Yet, graphs can be very different
from one another as they do not share the same properties (size, connectivity, communities, etc.) and
building a system able to manage graphs should take into account this diversity. A big challenge
concerning graph management is to design a system providing a scalable persistent storage and allowing
efficient browsing. Mainly to study social graphs, the most recent developments in graph partitioning
research often consider scalefree graphs. As we are interested in modelling connected objects and their
context, we focus on partitioning geometric graphs. Consequently our strategy differs, we consider
geometry as our main partitioning tool. In fact, we rely on Inverse Spacefilling Partitioning, a technique
which relies on a space filling curve to partition a graph and was previously applied to graphs essentially
generated from Meshes. Furthermore, we extend Inverse SpaceFilling Partitioning toward a new target
we define as Wide Area Graphs. We provide an extended comparison with two stateoftheart graph
partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to
better understand and identify the use cases for which the ISP partitioning solution is best suited.
Experimentations show that in favourable contexts, edgecuts can be drastically reduced, going from more
34% using FENNEL to less than 1% using ISP.
Soyez le premier à aimer ceci
Graph structure is a very powerful tool to model system and represent their actual shape. For instance, modelling an infrastructure or social network naturally leads to graph. Yet, graphs can be very different from one another as they do not share the same properties (size, connectivity, communities, etc.) and building a system able to manage graphs should take into account this diversity. A big challenge concerning graph management is to design a system providing a scalable persistent storage and allowing efficient browsing. Mainly to study social graphs, the most recent developments in graph partitioning research often consider scalefree graphs. As we are interested in modelling connected objects and their context, we focus on partitioning geometric graphs. Consequently our strategy differs, we consider geometry as our main partitioning tool. In fact, we rely on Inverse Spacefilling Partitioning, a technique which relies on a space filling curve to partition a graph and was previously applied to graphs essentially generated from Meshes. Furthermore, we extend Inverse SpaceFilling Partitioning toward a new target we define as Wide Area Graphs. We provide an extended comparison with two stateoftheart graph partitioning streaming strategies, namely LDG and FENNEL. We also propose customized metrics to better understand and identify the use cases for which the ISP partitioning solution is best suited. Experimentations show that in favourable contexts, edgecuts can be drastically reduced, going from more 34% using FENNEL to less than 1% using ISP.
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