Analysis of Influence Contribution in Social Advertising

Summary: ICA for influencer campaigns in OSNs is framed via Shapley-value rationale to quantify each influencer's contribution. It yields a linear-time exact solution under LT and a FPRAS for IC with scalable sampling, with experiments showing improved attribution and efficiency. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: he799753b781559f7
Venue: VLDB
Year: 2022
Pagerank: 5.7352831e-05
Overall Rank: 6,537 | 56.19%
DOI: 10.14778/3489496.3489514
PDF: Download (CC BY-NC-ND 4.0)

Incoming Non-self Citations Over Time

Authors

1. Yuqing Zhu (Nanyang Technological University)
2. Jing Tang (Hong Kong University of Science and Technology)
3. Xueyan Tang (Nanyang Technological University)
4. Lei Chen (Hong Kong University of Science and Technology)

BibTeX Citation

@article{zhu_vldb22,
        title = {{Analysis of Influence Contribution in Social Advertising}},
        author = {Zhu, Yuqing and Tang, Jing and Tang, Xueyan and Chen, Lei},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {2},
        pages = {348--360},
        doi = {10.14778/3489496.3489514},
        url = {https://doi.org/10.14778/3489496.3489514},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,846	Fast and Space-Efficient Parallel Algorithms for Influence Maximization	2024	VLDB	5.2498988e-05
9,759	Shapley Value Estimation Based on Differential Matrix	2025	SIGMOD	5.115551e-05
10,381	Approximating Contribution in Misinformation Mitigation	2027	SIGMOD	4.9605343e-05
10,492	ASSS: Adaptive Stratified Sampling for Shapley-like Values	2026	SIGMOD	4.9605343e-05
11,609	Influence Maximization via Vertex Countering	2024	VLDB	4.9605343e-05

Outgoing Citations (Sorted by Pagerank)

Showing 22 of 22 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
187	Influence Maximization: Near-Optimal Time Complexity Meets Practical Efficiency	2014	SIGMOD	0.00025945191
290	A Data-Based Approach to Social Influence Maximization	2012	VLDB	0.00021935235
305	Influence Maximization in Near-Linear Time: A Martingale Approach	2015	SIGMOD	0.00021567807
441	Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks	2016	SIGMOD	0.00018233755
1,396	Online Processing Algorithms for Influence Maximization	2018	SIGMOD	0.00010774759
1,545	Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study	2017	SIGMOD	0.00010270407
1,946	Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models	2016	SIGMOD	9.3059083e-05
2,171	Influence Maximization Revisited: Efficient Reverse Reachable Set Generation with Bound Tightened	2020	SIGMOD	8.9117572e-05
2,212	Real-time Targeted Influence Maximization for Online Advertisements	2015	VLDB	8.8427023e-05
2,300	Online Topic-Aware Influence Maximization	2015	VLDB	8.6638338e-05
2,589	From Competition to Complementarity: Comparative Influence Diffusion and Maximization	2016	VLDB	8.2390055e-05
2,698	Efficient Algorithms for Budgeted Influence Maximization on Massive Social Networks	2020	VLDB	8.1020179e-05
3,320	Revisiting the Stop-and-Stare Algorithms for Influence Maximization	2017	VLDB	7.4137064e-05
4,031	Real-Time Influence Maximization on Dynamic Social Streams	2017	VLDB	6.8273933e-05
4,053	Viral Marketing Meets Social Advertising: Ad Allocation with Minimum Regret	2015	VLDB	6.8146176e-05
4,469	Revenue Maximization in Incentivized Social Advertising	2017	VLDB	6.5725381e-05
4,635	Efficient Algorithms for Adaptive Influence Maximization	2018	VLDB	6.4757053e-05
5,209	Pricing Influential Nodes in Online Social Networks	2020	VLDB	6.2146527e-05
5,669	Efficient Approximation Algorithms for Adaptive Seed Minimization	2019	SIGMOD	6.0271116e-05
5,803	Continuous Influence Maximization: What Discounts Should We Offer to Social Network Users?	2016	SIGMOD	5.9716815e-05
5,849	The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches	2020	SIGMOD	5.9530603e-05
8,073	Coarsening Massive Influence Networks for Scalable Diffusion Analysis	2017	SIGMOD	5.3795016e-05

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