Fully dynamic algorithm for top-k densest subgraphs

Muhammad Anis Uddin Nasir, Aristides Gionis, Gianmarco Morales, Sarunas Girdzijauskas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Given a large graph, the densest-subgraph problem asks to find a subgraph with maximum average degree. When considering the top-k version of this problem, a nattive solution is to iteratively find the densest subgraph and remove it in each iteration. However, such a solution is impractical due to high processing cost. The problem is further complicated when dealing with dynamic graphs, since adding or removing an edge requires re-running the algorithm. In this paper, we study the top-k densest-subgraph problem in the sliding-window model and propose an efficient fully-dynamic algorithm. The input of our algorithm consists of an edge stream, and the goal is to find the node-disjoint subgraphs that maximize the sum of their densities. In contrast to existing state-of-the-art solutions that require iterating over the entire graph upon any update, our algorithm profits from the observation that updates only affect a limited region of the graph. Therefore, the top-k densest subgraphs are maintained by only applying local updates. We provide a theoretical analysis of the proposed algorithm and show empirically that the algorithm offen generates denser subgraphs than state-of-the-art competitors. Experiments show an improvement in efficiency of up to five orders of magnitude compared to state-of-the-art solutions.

Original languageEnglish
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1817-1826
Number of pages10
VolumePart F131841
ISBN (Electronic)9781450349185
DOIs
Publication statusPublished - 6 Nov 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: 6 Nov 201710 Nov 2017

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
CountrySingapore
CitySingapore
Period6/11/1710/11/17

Fingerprint

Top-k
Graph
Node
Sliding window
Competitors
Costs
Profit
Experiment
Theoretical analysis

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Cite this

Nasir, M. A. U., Gionis, A., Morales, G., & Girdzijauskas, S. (2017). Fully dynamic algorithm for top-k densest subgraphs. In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management (Vol. Part F131841, pp. 1817-1826). Association for Computing Machinery. https://doi.org/10.1145/3132847.3132966

Fully dynamic algorithm for top-k densest subgraphs. / Nasir, Muhammad Anis Uddin; Gionis, Aristides; Morales, Gianmarco; Girdzijauskas, Sarunas.

CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. p. 1817-1826.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Nasir, MAU, Gionis, A, Morales, G & Girdzijauskas, S 2017, Fully dynamic algorithm for top-k densest subgraphs. in CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. vol. Part F131841, Association for Computing Machinery, pp. 1817-1826, 26th ACM International Conference on Information and Knowledge Management, CIKM 2017, Singapore, Singapore, 6/11/17. https://doi.org/10.1145/3132847.3132966
Nasir MAU, Gionis A, Morales G, Girdzijauskas S. Fully dynamic algorithm for top-k densest subgraphs. In CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841. Association for Computing Machinery. 2017. p. 1817-1826 https://doi.org/10.1145/3132847.3132966
Nasir, Muhammad Anis Uddin ; Gionis, Aristides ; Morales, Gianmarco ; Girdzijauskas, Sarunas. / Fully dynamic algorithm for top-k densest subgraphs. CIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management. Vol. Part F131841 Association for Computing Machinery, 2017. pp. 1817-1826
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