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On Sampling Nodes in a Network

Authors:

Flavio Chiericetti,

Anirban Dasgupta,

Silvio Lattanzi,

Tamás SarlósAuthors Info & Claims

WWW '16: Proceedings of the 25th International Conference on World Wide Web

Pages 471 - 481

https://doi.org/10.1145/2872427.2883045

Published: 11 April 2016 Publication History

Abstract

Random walk is an important tool in many graph mining applications including estimating graph parameters, sampling portions of the graph, and extracting dense communities. In this paper we consider the problem of sampling nodes from a large graph according to a prescribed distribution by using random walk as the basic primitive. Our goal is to obtain algorithms that make a small number of queries to the graph but output a node that is sampled according to the prescribed distribution. Focusing on the uniform distribution case, we study the query complexity of three algorithms and show a near-tight bound expressed in terms of the parameters of the graph such as average degree and the mixing time. Both theoretically and empirically, we show that some algorithms are preferable in practice than the others. We also extend our study to the problem of sampling nodes according to some polynomial function of their degrees; this has implications for designing efficient algorithms for applications such as triangle counting.

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Cited By

Hasegawa THironaka SShudo K(2024)Estimation of Graph Features Based on Random Walks Using Neighbors’ PropertiesWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0567-5_32(456-466)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-981-96-0567-5_32
Preti GDe Francisci Morales GRiondato M(2023)MaNIACS: Approximate Mining of Frequent Subgraph Patterns through SamplingACM Transactions on Intelligent Systems and Technology10.1145/358725414:3(1-29)Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1145/3587254
Cousins CWohlgemuth CRiondato M(2023) Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher AveragesACM Transactions on Knowledge Discovery from Data10.1145/357702117:6(1-47)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1145/3577021
Show More Cited By

Index Terms

On Sampling Nodes in a Network
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
      1. Social network analysis
2. Theory of computation
  1. Design and analysis of algorithms
    1. Streaming, sublinear and near linear time algorithms
      1. Sketching and sampling
  2. Randomness, geometry and discrete structures
    1. Random walks and Markov chains

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WWW '16: Proceedings of the 25th International Conference on World Wide Web

April 2016

1482 pages

ISBN:9781450341431

General Chairs:
Jacqueline Bourdeau
Tele-university (TELUQ), Montreal, QC, Canada
,
Jim A. Hendler
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Roger Nkambou Nkambou
Université du Québec à Montréal, Montreal, QC, Canada
,
Program Chairs:
Ian Horrocks
University of Oxford, UK
,
Ben Y. Zhao
University of California at Santa Barbara, CA, USA

Copyright © 2016 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Sponsors

IW3C2: International World Wide Web Conference Committee

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 11 April 2016

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Research-article

Funding Sources

SIR Grant
Google Focused Research Award
ERC Starting Grant DMAP
Google Faculty Research Award

Conference

WWW '16

Sponsor:

IW3C2

WWW '16: 25th International World Wide Web Conference

April 11 - 15, 2016

Québec, Montréal, Canada

Acceptance Rates

WWW '16 Paper Acceptance Rate 115 of 727 submissions, 16%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Hasegawa THironaka SShudo K(2024)Estimation of Graph Features Based on Random Walks Using Neighbors’ PropertiesWeb Information Systems Engineering – WISE 202410.1007/978-981-96-0567-5_32(456-466)Online publication date: 3-Dec-2024
https://doi.org/10.1007/978-981-96-0567-5_32
Preti GDe Francisci Morales GRiondato M(2023)MaNIACS: Approximate Mining of Frequent Subgraph Patterns through SamplingACM Transactions on Intelligent Systems and Technology10.1145/358725414:3(1-29)Online publication date: 13-Apr-2023
https://dl.acm.org/doi/10.1145/3587254
Cousins CWohlgemuth CRiondato M(2023) Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher AveragesACM Transactions on Knowledge Discovery from Data10.1145/357702117:6(1-47)Online publication date: 6-Mar-2023
https://dl.acm.org/doi/10.1145/3577021
Nakajima KShudo K(2023)Random Walk Sampling in Social Networks Involving Private NodesACM Transactions on Knowledge Discovery from Data10.1145/356138817:4(1-28)Online publication date: 24-Feb-2023
https://dl.acm.org/doi/10.1145/3561388
Zhang LZhang ZWang GYuan Y(2023)Efficiently Sampling and Estimating Hypergraphs By Hybrid Random Walk2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00102(1273-1285)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00102
Tětek JThorup MLeonardi SGupta A(2022)Edge sampling and graph parameter estimation via vertex neighborhood accessesProceedings of the 54th Annual ACM SIGACT Symposium on Theory of Computing10.1145/3519935.3520059(1116-1129)Online publication date: 9-Jun-2022
https://dl.acm.org/doi/10.1145/3519935.3520059
Ben-Eliezer OEden TOren JFotakis DSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Sampling Multiple Nodes in Large NetworksProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498383(37-47)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498383
Ponciano JLinhares CRocha LFaria ETravençolo BHong JBures MPark JCerny T(2022)Combining clutter reduction methods for temporal network visualizationProceedings of the 37th ACM/SIGAPP Symposium on Applied Computing10.1145/3477314.3507018(1748-1755)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3477314.3507018
Wang RLi YLin SWu WXie HXu YLui J(2022)Common Neighbors Matter: Fast Random Walk Sampling with Common Neighbor AwarenessIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3150427(1-1)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3150427
Nakajima KShudo K(2022)Social Graph Restoration via Random Walk Sampling2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00065(01-14)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00065
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