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Efficient Probabilistic Truss Indexing on Uncertain Graphs

Published: 03 June 2021 Publication History

Abstract

Networks in many real-world applications come with an inherent uncertainty in their structure, due to e.g., noisy measurements, inference and prediction models, or for privacy purposes. Modeling and analyzing uncertain graphs has attracted a great deal of attention. Among the various graph analytic tasks studied, the extraction of dense substructures, such as cores or trusses, has a central role.
In this paper, we study the problem of (k, γ)-truss indexing and querying over an uncertain graph . A (k, γ)-truss is the largest subgraph of, such that the probability of each edge being contained in at least k − 2 triangles is no less than γ. Our first proposal, CPT-index, keeps all the (k, γ)-trusses: retrieval for any given k and γ can be executed in an optimal linear time w.r.t. the graph size of the queried (k, γ)-truss. We develop a bottom-up CPT-indexconstruction scheme and an improved algorithm for fast CPT-indexconstruction using top-down graph partitions. For trading off between (k, γ)-truss offline indexing and online querying, we further develop an approximate indexing approach (ϵ, Δr)-APXequipped with two parameters, ϵ and Δr, that govern tolerated errors.
Extensive experiments using large-scale uncertain graphs with 261 million edges validate the efficiency of our proposed indexing and querying algorithms against state-of-the-art methods.

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cover image ACM Conferences
WWW '21: Proceedings of the Web Conference 2021
April 2021
4054 pages
ISBN:9781450383127
DOI:10.1145/3442381
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 June 2021

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Author Tags

  1. indexing
  2. probabilistic k-truss
  3. query
  4. uncertain graph

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WWW '21
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WWW '21: The Web Conference 2021
April 19 - 23, 2021
Ljubljana, Slovenia

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Cited By

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  • (2024)Space-Efficient Indexes for Uncertain Strings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00367(4828-4842)Online publication date: 13-May-2024
  • (2024)Adaptive Truss Maximization on Large Graphs: A Minimum Cut Approach2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00253(3270-3282)Online publication date: 13-May-2024
  • (2024)Truss community search in uncertain graphsKnowledge and Information Systems10.1007/s10115-024-02215-266:12(7739-7773)Online publication date: 1-Dec-2024
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  • (2023)Most Probable Densest Subgraphs2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00115(1447-1460)Online publication date: Apr-2023
  • (2022)Reliable Community Search on Uncertain Graphs2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00092(1166-1179)Online publication date: May-2022
  • (2022)Nucleus Decomposition in Probabilistic Graphs: Hardness and Algorithms2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00021(218-231)Online publication date: May-2022

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