THESIS
2014
ix, 48 pages : illustrations ; 30 cm
Abstract
With the popular usage of graphs in many applications, such as social networks
analysis and web graph mining, how to store the graphs effectively in a distributed
environment is quite challenging and useful. The straightforward solution is to compress
the graphs. However, in this paper, we argue that the compressed graphs must
be able to handle atomic operations and real-time updates without decompressing
the graph. Unfortunately, the traditional compression methods cannot fulfill these
requirements. Thus, in this paper, we propose a novel and effective compression
method to compress distributed large graphs. Specifically, we first select a set of
central nodes and then start compressing the selected nodes's neighbourhood structure
by graph labeled trees (GLT), which are unive...[
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With the popular usage of graphs in many applications, such as social networks
analysis and web graph mining, how to store the graphs effectively in a distributed
environment is quite challenging and useful. The straightforward solution is to compress
the graphs. However, in this paper, we argue that the compressed graphs must
be able to handle atomic operations and real-time updates without decompressing
the graph. Unfortunately, the traditional compression methods cannot fulfill these
requirements. Thus, in this paper, we propose a novel and effective compression
method to compress distributed large graphs. Specifically, we first select a set of
central nodes and then start compressing the selected nodes's neighbourhood structure
by graph labeled trees (GLT), which are universally effective for all graphs and
self-descriptive so that no extra indices or dictionaries are involved. The extensive
experiments verify the effectiveness and efficiency of the proposed solution.
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