Data-driven efficient network and surveillance-based immunization

Zhang, Yao; Ramanathan, Arvind; Vullikanti, Anil; Pullum, Laura L.; Prakash, B. Aditya

doi:10.1007/s10115-018-01326-x

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…All in all, influence maximization research is of great value for many real-world applications including online marketing [41], trending topic detection [42], and information summarization [43]. Nevertheless, information diffusion has also been analysed from an influence minimization perspective and applied in areas such as public health policies [44], misinformation [45], and cybersecurity [26,38,39]. For instance, Jia et al [38] proposed limiting the propagation of adversarial content through the deletion of critical nodes and edges in a network.…”

Section: B Information Diffusion In Osnsmentioning

confidence: 99%

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Ferreyra,

Hecking,

Aïmeur

et al. 2021

Preprint

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Access-Control Lists (ACLs) (a.k.a. "friend lists") are one of the most important privacy features of Online Social Networks (OSNs) as they allow users to restrict the audience of their publications. Nevertheless, creating and maintaining custom ACLs can introduce a high cognitive burden on average OSNs users since it normally requires assessing the trustworthiness of a large number of contacts. In principle, community detection algorithms can be leveraged to support the generation of ACLs by mapping a set of examples (i.e. contacts labelled as "untrusted") to the emerging communities inside the user's ego-network. However, unlike users' access-control preferences, traditional community-detection algorithms do not take the homophily characteristics of such communities into account (i.e. attributes shared among members). Consequently, this strategy may lead to inaccurate ACL configurations and privacy breaches under certain homophily scenarios. This work investigates the use of community-detection algorithms for the automatic generation of ACLs in OSNs. Particularly, it analyses the performance of the aforementioned approach under different homophily conditions through a simulation model. Furthermore, since private information may reach the scope of untrusted recipients through the re-sharing affordances of OSNs, information diffusion processes are also modelled and taken explicitly into account. Altogether, the removal of gatekeeper nodes is further explored as a strategy to counteract unwanted data dissemination.

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Section: B Information Diffusion In Osnsmentioning

confidence: 99%

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Ferreyra,

Hecking,

Aïmeur

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Community detection for access-control decisions: Analysing the role of homophily and information diffusion in Online Social Networks

Ferreyra

Hecking

Aïmeur

et al. 2022

Online Social Networks and Media

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Scalable Misinformation Mitigation in Social Networks Using Reverse Sampling

Simpson

Srinivasan

Thomo

2022

The Computer Journal

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We consider misinformation propagating through a social network and study the problem of its prevention. The goal is to identify a set of $k$ users that need to be convinced to adopt a limiting campaign so as to minimize the number of people that end up adopting the misinformation. This work presents Reverse Prevention Sampling (RPS), an algorithm that provides a scalable solution to the misinformation mitigation problem. Our theoretical analysis shows that RPS runs in $O((k + l)(n + m)(\frac{1}{1 - \gamma }) \log n / \epsilon ^2 )$ expected time and returns a $(1 - 1/e - \epsilon )$-approximate solution with at least $1 - n^{-l}$ probability (where $\gamma $ is a typically small network parameter and $l$ is a confidence parameter). The time complexity of RPS substantially improves upon the previously best-known algorithms that run in time $\Omega (m n k \cdot POLY(\epsilon ^{-1}))$. We experimentally evaluate RPS on large datasets and show that it outperforms the state-of-the-art solution by several orders of magnitude in terms of running time. This demonstrates that misinformation mitigation can be made practical while still offering strong theoretical guarantees.

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Data-driven efficient network and surveillance-based immunization

Cited by 5 publications

References 30 publications

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

Community detection for access-control decisions: Analysing the role of homophily and information diffusion in Online Social Networks

Scalable Misinformation Mitigation in Social Networks Using Reverse Sampling

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