Abstract

Network connectivity optimization, which aims to manipulate network connectivity by changing its underlying topology, is a fundamental task behind a wealth of high-impact data mining applications, ranging from immunization, critical infrastructure construction, social collaboration mining, bioinformatics analysis, to intelligent transportation system design. To tackle its exponential computation complexity, greedy algorithms have been extensively used for network connectivity optimization by exploiting its diminishing returns property. Despite the empirical success, two key challenges largely remain open. First, on the theoretic side, the hardness, as well as the approximability of the general network connectivity optimization problem are still nascent except for a few special instances. Second, on the algorithmic side, current algorithms are often hard to balance between the optimization quality and the computational efficiency. In this paper, we systematically address these two challenges for the network connectivity optimization problem. First, we reveal some fundamental limits by proving that, for a wide range of network connectivity optimization problems, (1) they are NP-hard and (2) (1 - 1/e) is the optimal approximation ratio for any polynomial algorithms. Second, we propose an effective, scalable and general algorithm (CONTAIN) to carefully balance the optimization quality and the computational efficiency.

Original languageEnglish (US)
Title of host publicationKDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages1167-1176
Number of pages10
ISBN (Print)9781450355520
DOIs
StatePublished - Jul 19 2018
Event24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018 - London, United Kingdom
Duration: Aug 19 2018Aug 23 2018

Other

Other24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018
CountryUnited Kingdom
CityLondon
Period8/19/188/23/18

Fingerprint

Computational efficiency
Immunization
Critical infrastructures
Bioinformatics
Data mining
Hardness
Systems analysis
Topology
Polynomials

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Chen, C., Ying, L., Peng, R., & Tong, H. (2018). Network connectivity optimization: Fundamental limits and effective algorithms. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1167-1176). Association for Computing Machinery. https://doi.org/10.1145/3219819.3220019

Network connectivity optimization : Fundamental limits and effective algorithms. / Chen, Chen; Ying, Lei; Peng, Ruiyue; Tong, Hanghang.

KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. p. 1167-1176.

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

Chen, C, Ying, L, Peng, R & Tong, H 2018, Network connectivity optimization: Fundamental limits and effective algorithms. in KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, pp. 1167-1176, 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2018, London, United Kingdom, 8/19/18. https://doi.org/10.1145/3219819.3220019
Chen C, Ying L, Peng R, Tong H. Network connectivity optimization: Fundamental limits and effective algorithms. In KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2018. p. 1167-1176 https://doi.org/10.1145/3219819.3220019
Chen, Chen ; Ying, Lei ; Peng, Ruiyue ; Tong, Hanghang. / Network connectivity optimization : Fundamental limits and effective algorithms. KDD 2018 - Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2018. pp. 1167-1176
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