skip to main content
10.1145/2740908.2744107acmotherconferencesArticle/Chapter ViewAbstractPublication PagesthewebconfConference Proceedingsconference-collections
other

Inferring Graphs from Cascades: A Sparse Recovery Framework

Published: 18 May 2015 Publication History

Abstract

In the Graph Inference problem, one seeks to recover the edges of an unknown graph from the observations of cascades propagating over this graph. We approach this problem from the sparse recovery perspective. We introduce a general model of cascades, including the voter model and the independent cascade model, for which we provide the first algorithm which recovers the graph's edges with high probability and O(s log m) measurements where s is the maximum degree of the graph and $m$ is the number of nodes. Furthermore, we show that our algorithm also recovers the edge weights (the parameters of the diffusion process) and is robust in the context of approximate sparsity. Finally we validate our approach empirically on synthetic graphs.

References

[1]
B. D. Abrahao, F. Chierichetti, R. Kleinberg, and A. Panconesi. Trace complexity of network inference. In KDD, pages 491--499, 2013.
[2]
H. Daneshmand, M. Gomez-Rodriguez, L. Song, and B. Schölkopf. Estimating diffusion network structures: Recovery conditions, sample complexity & soft-thresholding algorithm. In ICML, pages 793--801, 2014.
[3]
D. Kempe, J. M. Kleinberg, and É. Tardos. Maximizing the spread of influence through a social network. In KDD, pages 137--146, 2003.
[4]
S. N. Negahban, P. Ravikumar, M. J. Wrainwright, and B. Yu. A unified framework for high-dimensional analysis of m-estimators with decomposable regularizers. Statistical Science, 27(4):538--557, December 2012.
[5]
P. Netrapalli and S. Sanghavi. Learning the graph of epidemic cascades. SIGMETRICS Perform. Eval. Rev., 40(1):211--222, June 2012.

Cited By

View all
  • (2024)Efficient PAC learnability of dynamical systems over multilayer networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693760(41557-41581)Online publication date: 21-Jul-2024
  • (2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024
  • (2024)Diffusion pattern miningKnowledge and Information Systems10.1007/s10115-024-02254-967:2(1101-1129)Online publication date: 12-Oct-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
WWW '15 Companion: Proceedings of the 24th International Conference on World Wide Web
May 2015
1602 pages
ISBN:9781450334730
DOI:10.1145/2740908
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

  • IW3C2: International World Wide Web Conference Committee

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 May 2015

Check for updates

Author Tags

  1. graph inference
  2. influence cascades
  3. sparse recovery

Qualifiers

  • Other

Conference

WWW '15
Sponsor:
  • IW3C2

Acceptance Rates

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

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)3
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Efficient PAC learnability of dynamical systems over multilayer networksProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693760(41557-41581)Online publication date: 21-Jul-2024
  • (2024)A continuous-time diffusion model for inferring multi-layer diffusion networksApplied Intelligence10.1007/s10489-024-05620-w54:17-18(8200-8223)Online publication date: 24-Jun-2024
  • (2024)Diffusion pattern miningKnowledge and Information Systems10.1007/s10115-024-02254-967:2(1101-1129)Online publication date: 12-Oct-2024
  • (2023)Finite Sample Analysis for Structured Discrete System IdentificationIEEE Transactions on Automatic Control10.1109/TAC.2023.323624368:10(6345-6352)Online publication date: Oct-2023
  • (2022)On the Consistency of Maximum Likelihood Estimators for Causal Network IdentificationIEEE Control Systems Letters10.1109/LCSYS.2021.30536106(175-180)Online publication date: 2022
  • (2021)Causal Inference for Influence Propagation—Identifiability of the Independent Cascade ModelComputational Data and Social Networks10.1007/978-3-030-91434-9_2(15-26)Online publication date: 4-Dec-2021
  • (2020)Iterative learning of graph connectivity from partially-observed cascade samplesProceedings of the Twenty-First International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing10.1145/3397166.3409130(141-150)Online publication date: 11-Oct-2020
  • (2020)Influence Analysis in Evolving Networks: A SurveyIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.2934447(1-1)Online publication date: 2020
  • (2020)Statistical Estimation of Diffusion Network Topologies2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00060(625-636)Online publication date: Apr-2020
  • (2020)On the Consistency of Maximum Likelihood Estimators for Causal Network Identification2020 59th IEEE Conference on Decision and Control (CDC)10.1109/CDC42340.2020.9304475(990-995)Online publication date: 14-Dec-2020
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media