Online diffusion source detection in social networks

Haishuai Wang; Peng Zhang; Ling Chen; Huan Liu; Chengqi Zhang

doi:10.1109/IJCNN.2015.7280455

Online diffusion source detection in social networks

Haishuai Wang, Peng Zhang, Ling Chen, Huan Liu, Chengqi Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

Abstract

In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in social networks. In this paper, we combine online learning and regression-based detection methods for real-time diffusion source detection. Specifically, we propose a new ℓ₁ non-convex regression model as the learning function, and an Online Stochastic Sub-gradient algorithm (OSS for short). The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

Original language	English (US)
Title of host publication	2015 International Joint Conference on Neural Networks, IJCNN 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479919604
DOIs	https://doi.org/10.1109/IJCNN.2015.7280455
State	Published - Sep 28 2015
Event	International Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland Duration: Jul 12 2015 → Jul 17 2015

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2015-September

Conference

Conference	International Joint Conference on Neural Networks, IJCNN 2015
Country/Territory	Ireland
City	Killarney
Period	7/12/15 → 7/17/15

Keywords

Bismuth
Delays
Estimation
RNA
Real-time systems

ASJC Scopus subject areas

Software
Artificial Intelligence

Access to Document

10.1109/IJCNN.2015.7280455

Cite this

Wang, H., Zhang, P., Chen, L., Liu, H., & Zhang, C. (2015). Online diffusion source detection in social networks. In 2015 International Joint Conference on Neural Networks, IJCNN 2015 Article 7280455 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2015.7280455

Online diffusion source detection in social networks. / Wang, Haishuai; Zhang, Peng; Chen, Ling et al.
2015 International Joint Conference on Neural Networks, IJCNN 2015. Institute of Electrical and Electronics Engineers Inc., 2015. 7280455 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Wang, H, Zhang, P, Chen, L, Liu, H & Zhang, C 2015, Online diffusion source detection in social networks. in 2015 International Joint Conference on Neural Networks, IJCNN 2015., 7280455, Proceedings of the International Joint Conference on Neural Networks, vol. 2015-September, Institute of Electrical and Electronics Engineers Inc., International Joint Conference on Neural Networks, IJCNN 2015, Killarney, Ireland, 7/12/15. https://doi.org/10.1109/IJCNN.2015.7280455

@inproceedings{00ccadbac3784eb9b8ad063083877ed4,

title = "Online diffusion source detection in social networks",

abstract = "In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in social networks. In this paper, we combine online learning and regression-based detection methods for real-time diffusion source detection. Specifically, we propose a new ℓ1 non-convex regression model as the learning function, and an Online Stochastic Sub-gradient algorithm (OSS for short). The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.",

keywords = "Bismuth, Delays, Estimation, RNA, Real-time systems",

author = "Haishuai Wang and Peng Zhang and Ling Chen and Huan Liu and Chengqi Zhang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; International Joint Conference on Neural Networks, IJCNN 2015 ; Conference date: 12-07-2015 Through 17-07-2015",

year = "2015",

month = sep,

day = "28",

doi = "10.1109/IJCNN.2015.7280455",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2015 International Joint Conference on Neural Networks, IJCNN 2015",

}

TY - GEN

T1 - Online diffusion source detection in social networks

AU - Wang, Haishuai

AU - Zhang, Peng

AU - Chen, Ling

AU - Liu, Huan

AU - Zhang, Chengqi

PY - 2015/9/28

Y1 - 2015/9/28

N2 - In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in social networks. In this paper, we combine online learning and regression-based detection methods for real-time diffusion source detection. Specifically, we propose a new ℓ1 non-convex regression model as the learning function, and an Online Stochastic Sub-gradient algorithm (OSS for short). The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

AB - In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response of malicious information spreading in social networks. In this paper, we combine online learning and regression-based detection methods for real-time diffusion source detection. Specifically, we propose a new ℓ1 non-convex regression model as the learning function, and an Online Stochastic Sub-gradient algorithm (OSS for short). The proposed model is empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.

KW - Bismuth

KW - Delays

KW - Estimation

KW - RNA

KW - Real-time systems

UR - http://www.scopus.com/inward/record.url?scp=84951092450&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84951092450&partnerID=8YFLogxK

U2 - 10.1109/IJCNN.2015.7280455

DO - 10.1109/IJCNN.2015.7280455

M3 - Conference contribution

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2015 International Joint Conference on Neural Networks, IJCNN 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - International Joint Conference on Neural Networks, IJCNN 2015

Y2 - 12 July 2015 through 17 July 2015

ER -

Online diffusion source detection in social networks

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this