DBScholar

Back to papers

Approximating Contribution in Misinformation Mitigation

Summary: Defines a Shapley-value-grounded metric for fairly allocating misinformation-mitigation credit among influencers, conditioned on the observed cascade. VG-App uses unbiased, outcome-consistent node-wise sampling to approximate contributions efficiently, with provable accuracy guarantees. (summarized by gpt-6-luna on Oct 22 2026)

Paper ID
h6bb91531f3e0b26e
Venue
SIGMOD
Year
2027
Pagerank
4.9605343e-05
Overall Rank
10,381 | 30.42%
DOI
10.1145/3837096
PDF
Download (CC BY 4.0)

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

BibTeX Citation

@inproceedings{wang_sigmod27,
        title = {{Approximating Contribution in Misinformation Mitigation}},
        author = {Wang, Jinghao and Miao, Renzhe and Wu, Yanping and Wang, Xiaoyang and Zhang, Ying},
        series = {{SIGMOD} '27},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3837096},
        url = {https://dl.acm.org/doi/10.1145/3837096},
        year = {2027}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank Citing Paper Year Venue Pagerank
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 12 of 12 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers