Encyclopedia of Big Data Technologies

Living Edition
| Editors: Sherif Sakr, Albert Zomaya

(Web/Social) Graph Compression

Living reference work entry
DOI: https://doi.org/10.1007/978-3-319-63962-8_54-1



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 xy 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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Authors and Affiliations

  1. 1.Università degli Studi di MilanoMilanoItaly

Section editors and affiliations

  • Paolo Ferragina
    • 1
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly