Average Degree-Based Densest Subgraph Computation

  • Lijun Chang
  • Lu Qin
Part of the Springer Series in the Data Sciences book series (SSDS)


In this section, we study average degree-based densest subgraph computation, where average degree is usually referred to as the edge density in the literature. In Section 4.1, we give preliminaries of densest subgraphs. Approximation algorithms and exact algorithms for computing the densest subgraph of a large input graph will be discussed in Section 4.2 and in Section 4.3, respectively.


  1. 5.
    Bahman Bahmani, Ravi Kumar, and Sergei Vassilvitskii. Densest subgraph in streaming and mapreduce. PVLDB, 5(5):454–465, 2012.Google Scholar
  2. 6.
    Oana Denisa Balalau, Francesco Bonchi, T.-H. Hubert Chan, Francesco Gullo, and Mauro Sozio. Finding subgraphs with maximum total density and limited overlap. In Proc. of WSDM’15, pages 379–388, 2015.Google Scholar
  3. 9.
    Sayan Bhattacharya, Monika Henzinger, Danupon Nanongkai, and Charalampos E. Tsourakakis. Space- and time-efficient algorithm for maintaining dense subgraphs on one-pass dynamic streams. In Proc. of STOC’15, pages 173–182, 2015.Google Scholar
  4. 18.
    Moses Charikar. Greedy approximation algorithms for finding dense components in a graph. In Proc. APPROX’00, pages 84–95, 2000.Google Scholar
  5. 26.
    Maximilien Danisch, T.-H. Hubert Chan, and Mauro Sozio. Large scale density-friendly graph decomposition via convex programming. In Proc. of WWW’17, pages 233–242, 2017.Google Scholar
  6. 27.
    Alessandro Epasto, Silvio Lattanzi, and Mauro Sozio. Efficient densest subgraph computation in evolving graphs. In Proc. of WWW’15, pages 300–310, 2015.Google Scholar
  7. 29.
    Giorgio Gallo, Michael D. Grigoriadis, and Robert Endre Tarjan. A fast parametric maximum flow algorithm and applications. SIAM J. Comput., 18(1):30–55, 1989.MathSciNetCrossRefGoogle Scholar
  8. 34.
    Aristides Gionis and Charalampos E. Tsourakakis. Dense subgraph discovery: KDD 2015 tutorial. In Proc. of KDD’15, pages 2313–2314, 2015.Google Scholar
  9. 35.
    A. V. Goldberg. Finding a maximum density subgraph. Technical report, Berkeley, CA, USA, 1984.Google Scholar
  10. 36.
    Andrew V. Goldberg and Robert Endre Tarjan. A new approach to the maximum-flow problem. J. ACM, 35(4):921–940, 1988.Google Scholar
  11. 45.
    Samir Khuller and Barna Saha. On finding dense subgraphs. In Proc. of ICALP’09, pages 597–608, 2009.Google Scholar
  12. 63.
    Muhammad Anis Uddin Nasir, Aristides Gionis, Gianmarco De Francisci Morales, and Sarunas Girdzijauskas. Fully dynamic algorithm for top-k densest subgraphs. In Proc. of CIKM’17, pages 1817–1826, 2017.Google Scholar
  13. 70.
    Lu Qin, Rong-Hua Li, Lijun Chang, and Chengqi Zhang. Locally densest subgraph discovery. In Proc. of KDD’15, pages 965–974, 2015.Google Scholar
  14. 87.
    Nikolaj Tatti and Aristides Gionis. Density-friendly graph decomposition. In Proc. of WWW’15, pages 1089–1099, 2015.Google Scholar
  15. 96.
    Yubao Wu, Ruoming Jin, Jing Li, and Xiang Zhang. Robust local community detection: On free rider effect and its elimination. PVLDB, 8(7):798–809, 2015.Google Scholar
  16. 97.
    Yubao Wu, Ruoming Jin, Xiaofeng Zhu, and Xiang Zhang. Finding dense and connected subgraphs in dual networks. In Proc. of ICDE’15, pages 915–926, 2015.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Lijun Chang
    • 1
  • Lu Qin
    • 2
  1. 1.School of Computer ScienceThe University of SydneySydneyAustralia
  2. 2.Centre for Artificial IntelligenceUniversity of Technology SydneySydneyAustralia

Personalised recommendations