(Web/Social) Graph Compression
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In this section, we discuss the problem of representing large graphs in core memory using suitable compressed data structures; after defining the problem, we survey the most important techniques developed in the last decade to solve it, highlighting the involved trade-offs.
A graph is a pair (N, A), where N is the set of nodes and A ⊆ N × N is the set of arcs, that is, a directed adjacency relation. We use n for the number of nodes, that is, n = |V |, and write x→y when (x, y) ∈ A.
Many datasets come with a natural relational structure, that is, a graph, that contains a wealth of information about the data itself, and many data mining tasks can be accomplished from this information alone (e.g., detecting outlier elements, identifying interest groups, estimating measures of importance, and so on). Often, such tasks can be solved through suitable (sometimes,...
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