Abstract
To compress a graph, some methods rely on finding highly compressible structures, such as very dense subgraphs, and encode a graph by listing these structures compressed. However, structures can overlap, leading to encoding the same information multiple times. The method we propose deals with this issue, by identifying overlaps and encoding them only once. We have tested our method on various real-world graphs. The obtained results show that our approach is efficient and outperforms state of the art methods. The source code of our algorithms, together with some sample input instances, are available at https://gitlab.liris.cnrs.fr/fpitois/fgsp.git.
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References
Besta, M., Hoefler, T.: Survey and taxonomy of lossless graph compression and space-efficient graph representations. CoRR abs/1806.01799 (2018). http://arxiv.org/abs/1806.01799
Bloem, P., de Rooij, S.: Large-scale network motif analysis using compression. Data Min. Knowl. Discov. 34(5), 1421–1453 (Sep 2020). https://doi.org/10.1007/s10618-020-00691-y
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exper. 2008(10), P10008 (2008)
Kang, U., Faloutsos, C.: Beyond ’caveman communities’: hubs and spokes for graph compression and mining. In: Proceedings - IEEE International Conference on Data Mining, ICDM, pp. 300–309 (12 2011). https://doi.org/10.1109/ICDM.2011.26
Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in VLSI domain. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 7(1), 69–79 (1999)
Kiouche, A.E., Baste, J., Haddad, M., Seba, H.: A neighborhood-preserving graph summarization. CoRR abs/2101.11559 (2021). https://arxiv.org/abs/2101.11559
Koutra, D., Kang, U., Vreeken, J., Faloutsos, C.: Summarizing and understanding large graphs. Stat. Anal. Data Min. ASA Data Sci. J. 8(3), 183–202 (2015). https://doi.org/10.1002/sam.11267
Lagraa, S., Seba, H.: An efficient exact algorithm for triangle listing in large graphs. Data Min. Knowl. Discov. 30(5), 1350–1369 (2016). https://doi.org/10.1007/s10618-016-0451-4
Liu, Y., Safavi, T., Dighe, A., Koutra, D.: Graph summarization methods and applications: a survey. ACM Comput. Surv. 51(3) (2018). https://doi.org/10.1145/3186727
Liu, Y., Safavi, T., Shah, N., Koutra, D.: Reducing large graphs to small supergraphs: a unified approach. Soc. Netw. Anal. Min. 8(1), 1–18 (2018). https://doi.org/10.1007/s13278-018-0491-4
Navlakha, S., Rastogi, R., Shrivastava, N.: Graph summarization with bounded error. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 419–432. SIGMOD ’08, Association for Computing Machinery, New York, NY, USA (2008). https://doi.org/10.1145/1376616.1376661
Riondato, M., García-Soriano, D., Bonchi, F.: Graph summarization with quality guarantees. Data Min. Knowl. Discov. 31(2), 314–349 (2017). https://doi.org/10.1007/s10618-016-0468-8
Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015). http://networkrepository.com
Rossi, R.A., Zhou, R.: GraphZIP: a clique-based sparse graph compression method. J. Big Data 5(1), 1–14 (2018)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 587–596. ACM (2013)
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This work is funded by the French National Research Agency under grant ANR-20-CE23-0002.
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Pitois, F., Seba, H., Haddad, M. (2023). A Fine-Grained Structural Partitioning Approach to Graph Compression. In: Wrembel, R., Gamper, J., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2023. Lecture Notes in Computer Science, vol 14148. Springer, Cham. https://doi.org/10.1007/978-3-031-39831-5_36
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DOI: https://doi.org/10.1007/978-3-031-39831-5_36
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