Marginal Gains to Maximize Content Spread in Social Networks

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dc.contributor.VIAF56851698 (Wu, W)
dc.contributor.authorYang, W.
dc.contributor.authorMa, J.
dc.contributor.authorLi, Y.
dc.contributor.authorYan, R.
dc.contributor.authorYuan, Jing
dc.contributor.authorWu, Weili
dc.contributor.authorLi, D.
dc.contributor.utdAuthorYuan, Jing
dc.contributor.utdAuthorWu, Weili
dc.date.accessioned2020-04-03T21:57:33Z
dc.date.available2020-04-03T21:57:33Z
dc.date.issued2019-05-06
dc.descriptionDue to copyright restrictions and/or publisher's policy full text access from Treasures at UT Dallas is limited to current UTD affiliates (use the provided Link to Article).
dc.description.abstractThe growing importance of social network for sharing and spreading various contents is leading to the changes in the way of information diffusion. To what extent can social content be diffused highly depends on the size of seed nodes and connectivity of the network. If the seed set is predetermined, then the best way to maximize the content spread is to add connectivities among the users. The existing work shows the content spread maximization problem to be NP-hard. One of the difficulties of designing an effective and efficient algorithm for the content spread maximization problem lies in that the objective function we aim to maximize lacks submodularity. In our work, we formulate the maximize content spread problem from an incremental marginal gain perspective. Although the objective function we derive is not submodular, both submodular lower and upper bounds are constructed and proved. Therefore, we apply the sandwich framework and devise a marginal increment-based algorithm (MIS) that guarantees a data-dependent factor. Furthermore, a novel scalable content spread maximization algorithm influence ranking and fast adjustment (IRFA), which is based on the influence ranking of a single node and fast adjustment with each boosting step in the network, is proposed. Through extensive experiments, we demonstrate that both MIS and IRFA algorithms are effective and outperform other edge selection strategies.
dc.description.departmentErik Jonsson School of Engineering and Computer Science
dc.identifier.bibliographicCitationYang, W., J. Ma, Y. Li, R. Yan, et al. 2019. "Marginal Gains to Maximize Content Spread in Social Networks." IEEE Transactions on Computational Social Systems 6(3): 479-490, doi: 10.1109/TCSS.2019.2911865
dc.identifier.issn2329-924X
dc.identifier.issue3
dc.identifier.urihttp://dx.doi.org/10.1109/TCSS.2019.2911865
dc.identifier.urihttps://hdl.handle.net/10735.1/7823
dc.identifier.volume6
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights©2019 IEEE
dc.source.journalIEEE Transactions on Computational Social Systems
dc.subjectApproximation theory
dc.subjectContent spread
dc.subjectBoosting (Algorithms)
dc.subjectSocial networks
dc.subjectHeuristic algorithms
dc.titleMarginal Gains to Maximize Content Spread in Social Networks
dc.type.genrearticle

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