Co-Activity Maximization in Online Social Networks

Bibliographic Data

ID22107490
AuthorsDongyu Mao (0000-0001-8366-2402, The University of Texas at Dallas), Weili Wu (0000-0001-8747-6340, The University of Texas at Dallas), Ding-Zhu Du (The University of Texas at Dallas), Ding‐Zhu Du (0000-0002-7345-2185, The University of Texas at Dallas)
Year2024
Volume11
Issue1
Pages66-75
Publication date2024-02-01
Open AccessYes
TypeARTICLE
VenueIEEE Transactions on Computational Social Systems (JOURNAL)
Journal identifiersISSN: 2329-924X • E-ISSN: 2373-7476
PublisherInstitute of Electrical and Electronics Engineers (IEEE) (PUBLISHER)
DOI10.1109/tcss.2022.3213260
OpenAlexW4312423395
LanguageEN
Citations received1
References cited43

Social media with online social networks has risen to be a prevalent force in information diffusion and public discourse. Despite its popularity and convenience, social media has been criticized for contributing to societal and ideological polarization as the result of trapping users in an echo chamber and filter bubbles. An emerging line of research focuses on ways to redesign content or link recommendation algorithms to mitigate the polarization phenomenon. However, existing works mainly concentrate on node-level balancing, while omitting the balancing effect that can be incurred by edge interaction in social networks. In this article, we take the first step to study the problem (CoAM) that assuming two campaigns are present in a network, how we should select seeds for each so as to maximize the interaction/activity between the followers of two campaigns (co-activity) after the diffusion process is finished. We begin our analysis by showing the hardness of CoAM under two diffusion models that are generalized from wildly used diffusion models and its objective function is neither submodular nor supermodular. This encourages us to design a submodular function that acts as a lower bound to the objective, by exploiting which we are able to devise a greedy algorithm with a provable approximation guarantee. To overcome the #P-hardness of diffusion calculation, we further extend the notion of random reverse-reachable (RR) set to devise a scalable instantiation of our approximation algorithm. We experimentally demonstrate the quality of our approximation algorithm on datasets collected from real-world social networks

Algorithm · Approximation algorithm · Greedy algorithm · Mathematical optimization · Maximization · Popularity · Scalability · Social media · Submodular set function · World Wide Web · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Media and Politics · Theoretical Computer Science

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