Full diffusion history reconstruction in networks

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

4 Citations (Scopus)

Abstract

Diffusion processes in networks can be used to model many real-world processes. Analysis of diffusion traces can help us answer important questions such as the source of diffusion and the role of each node in the diffusion process. However, in large-scale networks, it is very expensive if not impossible to monitor the entire network to collect the complete diffusion trace. This paper considers diffusion history reconstruction from a partial observation and develops a greedy, step-by-step reconstruction algorithm. It is proved that the algorithm always produces a diffusion history that is consistent with the partial observation. Our experimental results based on real networks and real diffusion data show that the algorithm significantly outperforms some existing methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages707-716
Number of pages10
ISBN (Print)9781479999255
DOIs
StatePublished - Dec 22 2015
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: Oct 29 2015Nov 1 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
CountryUnited States
CitySanta Clara
Period10/29/1511/1/15

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Chen, Z., Tong, H., & Ying, L. (2015). Full diffusion history reconstruction in networks. In Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 (pp. 707-716). [7363815] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2015.7363815

Full diffusion history reconstruction in networks. / Chen, Zhen; Tong, Hanghang; Ying, Lei.

Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 707-716 7363815.

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

Chen, Z, Tong, H & Ying, L 2015, Full diffusion history reconstruction in networks. in Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015., 7363815, Institute of Electrical and Electronics Engineers Inc., pp. 707-716, 3rd IEEE International Conference on Big Data, IEEE Big Data 2015, Santa Clara, United States, 10/29/15. https://doi.org/10.1109/BigData.2015.7363815
Chen Z, Tong H, Ying L. Full diffusion history reconstruction in networks. In Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 707-716. 7363815 https://doi.org/10.1109/BigData.2015.7363815
Chen, Zhen ; Tong, Hanghang ; Ying, Lei. / Full diffusion history reconstruction in networks. Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 707-716
@inproceedings{794b79b0e6c240eaa044d884b3c45660,
title = "Full diffusion history reconstruction in networks",
abstract = "Diffusion processes in networks can be used to model many real-world processes. Analysis of diffusion traces can help us answer important questions such as the source of diffusion and the role of each node in the diffusion process. However, in large-scale networks, it is very expensive if not impossible to monitor the entire network to collect the complete diffusion trace. This paper considers diffusion history reconstruction from a partial observation and develops a greedy, step-by-step reconstruction algorithm. It is proved that the algorithm always produces a diffusion history that is consistent with the partial observation. Our experimental results based on real networks and real diffusion data show that the algorithm significantly outperforms some existing methods.",
author = "Zhen Chen and Hanghang Tong and Lei Ying",
year = "2015",
month = "12",
day = "22",
doi = "10.1109/BigData.2015.7363815",
language = "English (US)",
isbn = "9781479999255",
pages = "707--716",
booktitle = "Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Full diffusion history reconstruction in networks

AU - Chen, Zhen

AU - Tong, Hanghang

AU - Ying, Lei

PY - 2015/12/22

Y1 - 2015/12/22

N2 - Diffusion processes in networks can be used to model many real-world processes. Analysis of diffusion traces can help us answer important questions such as the source of diffusion and the role of each node in the diffusion process. However, in large-scale networks, it is very expensive if not impossible to monitor the entire network to collect the complete diffusion trace. This paper considers diffusion history reconstruction from a partial observation and develops a greedy, step-by-step reconstruction algorithm. It is proved that the algorithm always produces a diffusion history that is consistent with the partial observation. Our experimental results based on real networks and real diffusion data show that the algorithm significantly outperforms some existing methods.

AB - Diffusion processes in networks can be used to model many real-world processes. Analysis of diffusion traces can help us answer important questions such as the source of diffusion and the role of each node in the diffusion process. However, in large-scale networks, it is very expensive if not impossible to monitor the entire network to collect the complete diffusion trace. This paper considers diffusion history reconstruction from a partial observation and develops a greedy, step-by-step reconstruction algorithm. It is proved that the algorithm always produces a diffusion history that is consistent with the partial observation. Our experimental results based on real networks and real diffusion data show that the algorithm significantly outperforms some existing methods.

UR - http://www.scopus.com/inward/record.url?scp=84963728317&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84963728317&partnerID=8YFLogxK

U2 - 10.1109/BigData.2015.7363815

DO - 10.1109/BigData.2015.7363815

M3 - Conference contribution

AN - SCOPUS:84963728317

SN - 9781479999255

SP - 707

EP - 716

BT - Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

PB - Institute of Electrical and Electronics Engineers Inc.

ER -