TY - JOUR
T1 - Towards large-scale social networks with online diffusion provenance detection
AU - Wang, Haishuai
AU - Wu, Jia
AU - Pan, Shirui
AU - Zhang, Peng
AU - Chen, Ling
PY - 2017/2/26
Y1 - 2017/2/26
N2 - In this paper we study a new problem of online discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l1 non-convex regression model as the identification model. The proposed methods are 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 discovering diffusion provenances in large networks. Existing work on network diffusion provenance identification focuses on offline learning where data collected from network detectors are static and a snapshot of the network is available before learning. However, an offline learning model does not meet the need for early warning, real-time awareness, or a real-time response to malicious information spreading in networks. To this end, we propose an online regression model for real-time diffusion provenance identification. Specifically, we first use offline collected network cascades to infer the edge transmission weights, and then use an online l1 non-convex regression model as the identification model. The proposed methods are empirically evaluated on both synthetic and real-world networks. Experimental results demonstrate the effectiveness of the proposed model.
KW - L₁ Regression
KW - Online identification
KW - Social network
KW - Source locating
UR - http://www.scopus.com/inward/record.url?scp=84994750509&partnerID=8YFLogxK
UR - http://purl.org/au-research/grants/arc/DP140102206
UR - http://purl.org/au-research/grants/arc/DP140100545
U2 - 10.1016/j.comnet.2016.08.025
DO - 10.1016/j.comnet.2016.08.025
M3 - Article
AN - SCOPUS:84994750509
VL - 114
SP - 154
EP - 166
JO - Computer Networks
JF - Computer Networks
SN - 1389-1286
ER -