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Efficient maximum clique computation and enumeration over large sparse graphs

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Abstract

This paper studies the problem of maximum clique computation (MCC) over sparse graphs, as large real-world graphs are usually sparse. In the literature, the problem of MCC over sparse graphs has been studied separately and less extensively than its dense counterpart—MCC over dense graphs—and advanced algorithmic techniques that are developed for MCC over dense graphs have not been utilized in the existing MCC solvers for sparse graphs. In this paper, we design an algorithm \(\mathsf {MC\text {-}BRB}\) for sparse graphs which transforms an instance of MCC over a large sparse graph G to instances of k-clique finding (KCF) over dense subgraphs of G, each of which can be computed by the existing MCC solvers for dense graphs. To further improve the efficiency, we then develop a new branch-reduce-&-bound framework for KCF over dense graphs by proposing light-weight reducing techniques and leveraging the advanced branching and bounding techniques that are used in the existing MCC solvers for dense graphs. In addition, we also design an ego-centric algorithm \(\mathsf {MC\text {-}EGO}\) for heuristically computing a near-maximum clique in near-linear time, and we extend our \(\mathsf {MC\text {-}BRB}\) algorithm to enumerate all maximum cliques. Finally, we parallelize our algorithms to exploit multiple CPU cores. We conduct extensive empirical studies on large real graphs and demonstrate the efficiency and effectiveness of our techniques.

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Notes

  1. When processing large sparse graphs, although the adjacency matrix can be replaced by a hash set for memory efficiency, the quadratic (i.e., \(|V|^2\)) memory consumption is still inevitable; note that the state-of-the-art MCC-Dense solver \(\mathsf {MoMC}\) [34] also materializes the complement graph of the input graph, i.e., explicitly stores the non-neighbors of each vertex, for efficient processing.

  2. The source code of \(\mathsf {MC\text {-}BRB}\) is open sourced at https://github.com/LijunChang/MC-BRB.

  3. http://snap.stanford.edu/.

  4. http://law.di.unimi.it/datasets.php.

  5. http://networkrepository.com/.

  6. http://man7.org/linux/man-pages/man1/time.1.html.

  7. The source code of \(\mathsf {MC\text {-}BRB}\) is open sourced at https://github.com/LijunChang/MC-BRB.

  8. The source code of \(\mathsf {PMC}\) is downloaded from https://github.com/ryanrossi/pmc.

  9. The binary code of \(\mathsf {BBMCSP}\) is downloaded from http://venus.elai.upm.es/logs/results_sparse/bin/bbmcsp_linux_release.

  10. The source code of \(\mathsf {RMC}\) is obtained from the authors of [35].

  11. The adjacency lists are represented by compressed bit strings in \(\mathsf {BBMCSP}\), and represented by arrays (specifically, C++ vectors) in \(\mathsf {PMC}\) and \(\mathsf {RMC}\).

  12. The source code of \(\mathsf {MoMC}\) is downloaded from https://home.mis.u-picardie.fr/~cli/MoMC2016.c.

  13. https://turing.cs.hbg.psu.edu/txn131/clique.html.

  14. https://github.com/sparsehash/sparsehash.

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Acknowledgements

The author is supported by ARC DP160101513 and FT180100256.

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  1. School of Computer Science, The University of Sydney, Sydney, Australia

    Lijun Chang

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Chang, L. Efficient maximum clique computation and enumeration over large sparse graphs. The VLDB Journal 29, 999–1022 (2020). https://doi.org/10.1007/s00778-020-00602-z

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