Qintian Guo
Abstract
Given a graph G, a cost associated with each node, and a budget B, the budgeted influence maximization (BIM) aims to find the optimal set S of seed nodes that maximizes the influence among all possible sets such that the total cost of nodes in S is no larger than B. Existing solutions mainly follow the non-adaptive idea, i.e., determining all the seeds before observing any actual diffusion. Due to the absence of actual diffusion information, they may result in unsatisfactory influence spread. Motivated by the limitation of existing solutions, in this paper, we make the first attempt to solve the BIM problem under the adaptive setting, where seed nodes are iteratively selected after observing the diffusion result of the previous seeds. We design the first practical algorithm which achieves an expected approximation guarantee by probabilistically adopting a cost-aware greedy idea or a single influential node. Further, we develop an optimized version to improve its practical performance in terms of influence spread.
Besides, the scalability issues of the adaptive IM-related problems still remain open. It is because they usually involve multiple rounds (e.g., equal to the number of seeds) and in each round, they have to construct sufficient new reverse-reachable set (RR-set) samples such that the claimed approximation guarantee can actually hold. However, this incurs prohibitive computation, imposing limitations on real applications. To solve this dilemma, we propose an incremental update approach. Specifically, it maintains extra construction information when building RR-sets, and then it can quickly correct a problematic RR-set from the very step where it is first affected. As a result, we recycle the RR-sets at a small computational cost, while still providing correctness guarantee. Finally, extensive experiments on large-scale real graphs demonstrate the superiority of our algorithms over baselines in terms of both influence spread and running time.
- Cigdem Aslay, Francesco Bonchi, Laks VS Lakshmanan, and Wei Lu. 2017. Revenue Maximization in Incentivized Social Advertising. PVLDB, Vol. 10, 11 (2017), 1238--1249.Google Scholar
Digital Library
- Ashwinkumar Badanidiyuru and Jan Vondrá k. 2014. Fast algorithms for maximizing submodular functions. In SODA. 1497--1514.Google Scholar
- Prithu Banerjee, Wei Chen, and Laks VS Lakshmanan. 2020. Maximizing social welfare in a competitive diffusion model. PVLDB, Vol. 14, 4 (2020), 613--625.Google ScholarDigital Library
- Shishir Bharathi, David Kempe, and Mahyar Salek. 2007. Competitive influence maximization in social networks. In WINE. 306--311.Google Scholar
- Song Bian, Qintian Guo, Sibo Wang, and Jeffrey Xu Yu. 2020. Efficient Algorithms for Budgeted Influence Maximization on Massive Social Networks. PVLDB, Vol. 13, 9 (2020), 1498--1510.Google ScholarDigital Library
- Christian Borgs, Michael Brautbar, Jennifer T. Chayes, and Brendan Lucier. 2014. Maximizing Social Influence in Nearly Optimal Time. In SODA. 946--957.Google Scholar
- Tiantian Chen, Jianxiong Guo, and Weili Wu. 2022. Adaptive multi-feature budgeted profit maximization in social networks. SNAM, Vol. 12, 1 (2022), 164.Google Scholar
- Wei Chen, Chi Wang, and Yajun Wang. 2010. Scalable influence maximization for prevalent viral marketing in large-scale social networks. In SIGKDD. 1029--1038.Google Scholar
- Pedro Domingos and Matt Richardson. 2001. Mining the network value of customers. In SIGKDD. 57--66.Google Scholar
- Chen Feng, Luoyi Fu, Bo Jiang, Haisong Zhang, Xinbing Wang, Feilong Tang, and Guihai Chen. 2020. Neighborhood matters: Influence maximization in social networks with limited access. IEEE Trans. Knowl. Data Eng., Vol. 34, 6 (2020), 2844--2859.Google Scholar
- Flaminjoy. 2022. Influencer Marketing Budgets. https://flaminjoy.com/blog/influencer-marketing-budget/.Google Scholar
- Forbes. 2021. One In Four Influencers Bought Fake Followers. https://www.forbes.com/sites/forbestechcouncil/2022/09/09/new-study-one-in-four-influencers-bought-fake-followers/'sh=3e3471056843.Google Scholar
- Sainyam Galhotra, Akhil Arora, and Shourya Roy. 2016. Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models. In SIGMOD. 743--758.Google Scholar
- Daniel Golovin and Andreas Krause. 2011. Adaptive submodularity: Theory and applications in active learning and stochastic optimization. J. Artif. Intell. Res., Vol. 42 (2011), 427--486.Google ScholarCross Ref
- Qintian Guo, Sibo Wang, Zhewei Wei, and Ming Chen. 2020. Influence Maximization Revisited: Efficient Reverse Reachable Set Generation with Bound Tightened. In SIGMOD. 2167--2181.Google Scholar
- Qintian Guo, Sibo Wang, Zhewei Wei, Wenqing Lin, and Jing Tang. 2022. Influence Maximization Revisited: Efficient Sampling with Bound Tightened. ACM Trans. Database Syst., Vol. 47, 3 (2022), 12:1--12:45.Google ScholarDigital Library
- Kai Han, Keke Huang, Xiaokui Xiao, Jing Tang, Aixin Sun, and Xueyan Tang. 2018a. Efficient Algorithms for Adaptive Influence Maximization. PVLDB, Vol. 11, 9 (2018), 1029--1040.Google ScholarDigital Library
- Kai Han, Chaoting Xu, Fei Gui, Shaojie Tang, He Huang, and Jun Luo. 2018b. Discount allocation for revenue maximization in online social networks. In MobiHoc. 121--130.Google Scholar
- Shuo Han, Fuzhen Zhuang, Qing He, and Zhongzhi Shi. 2014. Balanced seed selection for budgeted influence maximization in social networks. In PAKDD. 65--77.Google Scholar
- Xinran He, Ke Xu, David Kempe, and Yan Liu. 2016. Learning influence functions from incomplete observations. NeurIPS, Vol. 29 (2016).Google Scholar
- Keke Huang, Jing Tang, Kai Han, Xiaokui Xiao, Wei Chen, Aixin Sun, Xueyan Tang, and Andrew Lim. 2020. Efficient approximation algorithms for adaptive influence maximization. VLDBJ, Vol. 29, 6 (2020), 1385--1406.Google ScholarDigital Library
- Keke Huang, Sibo Wang, Glenn Bevilacqua, Xiaokui Xiao, and Laks VS Lakshmanan. 2017. Revisiting the stop-and-stare algorithms for influence maximization. PVLDB, Vol. 10, 9 (2017), 913--924.Google ScholarDigital Library
- IZEA. 2021a. GUESS Eyewear. https://izea.com/resources/case-studies/guess-eyewear.Google Scholar
- IZEA. 2021b. The Ultimate Guide to Influencer Marketing. https://izea.com/resources/influencer-marketing/.Google Scholar
- IZEA. 2021c. Visit Tampa Bay. https://izea.com/resources/case-studies/visit-tampa-bay.Google Scholar
- David Kempe, Jon Kleinberg, and Éva Tardos. 2003. Maximizing the spread of influence through a social network. In SIGKDD. 137--146.Google Scholar
- Samir Khuller, Anna Moss, and Joseph Seffi Naor. 1999. The budgeted maximum coverage problem. Inf. Process. Lett., Vol. 70, 1 (1999), 39--45.Google ScholarDigital Library
- Andreas Krause and Carlos Guestrin. 2005. A note on the budgeted maximization of submodular functions. Citeseer.Google Scholar
- Jure Leskovec and Andrej Krevl. 2014. SNAP Datasets: Stanford Large Network Dataset Collection. http://snap.stanford.edu/data.Google Scholar
- Yuchen Li, Ju Fan, George Ovchinnikov, and Panagiotis Karras. 2019. Maximizing multifaceted network influence. In ICDE. 446--457.Google Scholar
- LINQIA. 2019. How Much Budget Should You Spend on Influencer Marketing? https://www.linqia.com/insights/how-much-budget-should-you-spend-on-influencer-marketing/.Google Scholar
- Huy Nguyen and Rong Zheng. 2013. On budgeted influence maximization in social networks. IEEE J. Sel. Areas Commun., Vol. 31, 6 (2013), 1084--1094.Google ScholarCross Ref
- Hung T Nguyen, Thang N Dinh, and My T Thai. 2016. Cost-aware targeted viral marketing in billion-scale networks. In INFOCOM. 1--9.Google Scholar
- Srinivasan Parthasarathy. 2020. Adaptive Submodular Maximization under Stochastic Item Costs. In COLT. 3133--3151.Google Scholar
- Binghui Peng and Wei Chen. 2019. Adaptive Influence Maximization with Myopic Feedback. arXiv preprint arXiv:1905.11663 (2019).Google Scholar
- Canh V Pham, My T Thai, Hieu V Duong, Bao Q Bui, and Huan X Hoang. 2018. Maximizing misinformation restriction within time and budget constraints. J. Combin. Optim., Vol. 35, 4 (2018), 1202--1240.Google ScholarDigital Library
- Lior Seeman and Yaron Singer. 2013. Adaptive seeding in social networks. In FOCS. 459--468.Google Scholar
- Chonggang Song, Wynne Hsu, and Mong Li Lee. 2016. Targeted influence maximization in social networks. In CIKM. 1683--1692.Google Scholar
- Shopify Staff. 2023. Influencer Marketing Prices: How Much Should You Pay. https://www.shopify.com/blog/influencer-pricing.Google Scholar
- Lichao Sun, Weiran Huang, Philip S Yu, and Wei Chen. 2018. Multi-round influence maximization. In SIGKDD. 2249--2258.Google Scholar
- Jing Tang, Keke Huang, Xiaokui Xiao, Laks V. S. Lakshmanan, Xueyan Tang, Aixin Sun, and Andrew Lim. 2019. Efficient Approximation Algorithms for Adaptive Seed Minimization. In SIGMOD. 1096--1113.Google Scholar
- Jing Tang, Xueyan Tang, Xiaokui Xiao, and Junsong Yuan. 2018. Online Processing Algorithms for Influence Maximization. In SIGMOD. 991--1005.Google Scholar
- Youze Tang, Yanchen Shi, and Xiaokui Xiao. 2015. Influence maximization in near-linear time: A martingale approach. In SIGMOD. 1539--1554.Google Scholar
- Youze Tang, Xiaokui Xiao, and Yanchen Shi. 2014. Influence maximization: Near-optimal time complexity meets practical efficiency. In SIGMOD. 75--86.Google Scholar
- Guangmo Tong, Weili Wu, Shaojie Tang, and Ding-Zhu Du. 2016. Adaptive influence maximization in dynamic social networks. IEEE/ACM Trans. Netw., Vol. 25, 1 (2016), 112--125.Google ScholarDigital Library
- Sharan Vaswani and Laks VS Lakshmanan. 2016. Adaptive influence maximization in social networks: Why commit when you can adapt? arXiv preprint arXiv:1604.08171 (2016).Google Scholar
- Ke Xu and Kai Han. 2018. Cost-Aware Targeted Viral Marketing with Time Constraints in Social Networks. In CollaborateCom. 75--91.Google Scholar
- Yu Yang, Xiangbo Mao, Jian Pei, and Xiaofei He. 2016. Continuous influence maximization: What discounts should we offer to social network users?. In SIGMOD. 727--741.Google Scholar
- Jing Yuan and Shao-Jie Tang. 2017. Adaptive discount allocation in social networks. In MobiHoc. 1--10.Google Scholar
- Ping Zhang, Zhifeng Bao, Yuchen Li, Guoliang Li, Yipeng Zhang, and Zhiyong Peng. 2018. Trajectory-driven influential billboard placement. In SIGKDD. 2748--2757.Google Scholar
- Yipeng Zhang, Yuchen Li, Zhifeng Bao, Baihua Zheng, and HV Jagadish. 2021. Minimizing the regret of an influence provider. In SIGMOD. 2115--2127.Google Scholar
- Yapu Zhang, Xianliang Yang, Suixiang Gao, and Wenguo Yang. 2019. Budgeted profit maximization under the multiple products independent cascade model. Access, Vol. 7 (2019), 20040--20049.Google ScholarCross Ref
- Yuqing Zhu, Jing Tang, and Xueyan Tang. 2020. Pricing influential nodes in online social networks. PVLDB, Vol. 13, 10 (2020), 1614--1627.Google ScholarDigital Library
Index Terms
- Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach
Recommendations
Online Processing Algorithms for Influence Maximization
SIGMOD '18: Proceedings of the 2018 International Conference on Management of DataInfluence maximization is a classic and extensively studied problem with important applications in viral marketing. Existing algorithms for influence maximization, however, mostly focus on offline processing, in the sense that they do not provide any ...
Robust Influence Maximization
KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIn this paper, we address the important issue of uncertainty in the edge influence probability estimates for the well studied influence maximization problem --- the task of finding k seed nodes in a social network to maximize the influence spread. We ...
Efficient and effective influence maximization in social networks: A hybrid-approach
AbstractInfluence Maximization (IM) is the problem of finding a seed set composed of k nodes that maximize their influence spread over a social network. Kempe et al. showed the problem to be NP-hard and proposed a greedy ...
Comments