Paper Info
Title
Rooting out Rumor Sources in Online Social Networks: The Value of Diversity from Multiple Observations
Abstract
This paper addresses the problem of rumor source detection with multiple observations, from a statistical point of view of a spreading over a network, based on the susceptibleinfectious model. For tree networks, multiple independent observations can dramatically improve the detection probability. For the case of a single rumor source, we propose a unified inference framework based on the joint rumor centrality, and provide explicit detection performance for degree-regular tree networks. Surprisingly, even with merely two observations, the detection probability at least doubles that of a single observation, and further approaches one, i.e., reliable detection, with increasing degree. This indicates that a richer diversity enhances detectability. Furthermore, we consider the case of multiple connected sources and investigate the effect of diversity. For general graphs, a detection algorithm using a breadth-first search strategy is also proposed and evaluated. Besides rumor source detection, our results can be used in network forensics to combat recurring epidemic-like information spreading such as online anomaly and fraudulent email spams.
Year
DOI
Venue
2015
10.1109/JSTSP.2015.2389191
Selected Topics in Signal Processing, IEEE Journal of  
Keywords
Field
DocType
graph networks,inference algorithms,maximum likelihood detection,multiple observations,rumor spreading,detectors,silicon,network forensics,hidden markov models,reliability,digital forensics
Data mining,Graph,Social network,Network forensics,Computer science,Inference,Rumor,Centrality,Artificial intelligence,Hidden Markov model,Machine learning,Signal processing algorithms
Journal
Volume
Issue
ISSN
PP
99
1932-4553
Citations 
PageRank 
References 
1
0.36
12
Authors
4
Name
Order
Citations
PageRank
Zhaoxu Wang1592.88
dong wenxiang21037.55
Wenyi Zhang370562.34
Chee Wei Tan4136992.01