BLAG: Bandit On Large Action Set Graph
Yucheng Lu, Xudong Wu, Jingfan Meng, Luoyi Fu, Xinbing Wang

TL;DR
This paper introduces BLAG, a bandit-based method for adaptive diffusion of sensitive information in social networks, effectively reducing information loss and delaying cascades with limited knowledge of network topology.
Contribution
BLAG is the first adaptive diffusion model for sensitive information that learns user forwarding abilities in large, partially known networks with low complexity and provable efficiency.
Findings
BLAG reduces information loss by at least 40%.
BLAG achieves at least 10 times higher learning efficiency.
BLAG significantly delays cascading of sensitive information.
Abstract
Information diffusion in social networks facilitates rapid and large-scale propagation of content. However, spontaneous diffusion behavior could also lead to the cascading of sensitive information, which is neglected in prior arts. In this paper, we present the first look into adaptive diffusion of sensitive information, which we aim to prevent from widely spreading without incurring much information loss. We undertake the investigation in networks with partially known topology, meaning that some users' ability of forwarding information is unknown. Formulating the problem into a bandit model, we propose BLAG (Bandit on Large Action set Graph), which adaptively diffuses sensitive information towards users with weak forwarding ability that is learnt from tentative transmissions and corresponding feedbacks. BLAG enjoys a low complexity of O(n), and is provably more efficient in the sense…
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Taxonomy
TopicsComplex Network Analysis Techniques · Misinformation and Its Impacts · Advanced Bandit Algorithms Research
