Relaxed Clique Percolation and Disinformation-Resilient Domains for Social Commerce Networks
<|reference_start|>Relaxed Clique Percolation and Disinformation-Resilient Domains for Social Commerce Networks: Must we trace and block all fake content in a social commerce network so that genuine users may enjoy fake-free information? Such efforts largely fail, because, as we get better at spam detection, spammers use the same advances for anti-detection. As a fundamentally new approach, we show that an online platform can aggregate and route user-generated content in a smart personalized way, which fosters and relies on "collective social responsibility". We introduce the notion of information aggregation domain, or simply, domain: composed for a given "central" node (user account), a domain is a connected set of nodes whose user-generated content is eligible to be used to meet the central node's information needs. Admitting malicious information sources - "bad citizen" nodes - into "good citizen" nodes' domains puts the good citizens at risk for disinformation attacks. We show how a platform can limit this risk by exploiting the social link structure between its nodes without the need to know which nodes are good or bad citizens. We introduce Relaxed Clique Percolation (RCP), a class of policies to compose personalized disinformation-resilient domains. Then, we define "RCP cores" and show how they can be used to efficiently compose resilient domains for all network nodes at once. Finally, we analyze the properties of RCP domains found in real-world social networks including Slashdot, Facebook, Flickr, and Yelp, to affirm that in practice, RCP domains turn out to be large and spatially diverse.<|reference_end|>
@article{paul2024relaxed,
    title={Relaxed Clique Percolation and Disinformation-Resilient Domains for
  Social Commerce Networks},
    author={Himangshu Paul, Alexander Nikolaev},
    journal={arXiv preprint arXiv:2403.14767},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.14767},
    primaryClass={cs.SI}
}