Affiliation:
1. Guangzhou University, Chinese University of Hong Kong
2. Guangzhou University
3. University of New South Wale
4. ACEM, Shanghai Jiao Tong University
Abstract
Competitive viral marketing considers the product competition of multiple companies, where each user may adopt one product and propagate the product to other users. Existing studies focus on a traditional seeding strategy where a company only selects seeds from the users with no adopted product to maximize its influence (i.e., the number of users who will adopt its product). However, influential users are often rare, and the gain from traditional seeding will degrade as the number of seeds increases. Therefore, in this paper, we study the promising
countering
strategy which is to counter some users who initially use other products s.t. they will turn to adopting the target product and recommending it to others.
We propose the problem of
influence countering
: given a graph, a budget
b
, a target company
C
t
, and a set
S
of the seeds adopting different companies (where each seed adopts one company), we counter
b
users in
S
who do not adopt
C
t
to turn to adopt
C
t
s.t. the expected number of users who eventually adopt
C
t
in the influence diffusion is maximized. Following existing studies, we formalize the diffusion process by the Multi-Campaigner Independent Cascade model. We prove the influence countering problem is #P-complete and its influence computation is #P-hard. Then, we propose two novel algorithms
MIC
and
MIC
+
to address the problem. In general,
MIC
estimates seed influence by its empirical average influence in multiple graph samplings, while
MIC
+
improves
MIC
by reducing the cost of influence estimation and the required number of samples. Given pre-set
ε
and
l
, both algorithms return a (1 -
ε
)-approximate solution with at least 1 -
n
-
l
probability. We also design an index for
MIC
+
to efficiently process graphs that are frequently updated. The experiments on 8 real-world datasets show that our algorithms are efficient in practice while offering strong result quality.
Publisher
Association for Computing Machinery (ACM)
Reference52 articles.
1. Zahra Aghaee, Mohammad Mahdi Ghasemi, Hamid Ahmadi Beni, Asgarali Bouyer, and Afsaneh Fatemi. 2021. A survey on meta-heuristic algorithms for the influence maximization problem in the social networks. Computing (2021), 2437--2477.
2. Akhil Arora Sainyam Galhotra and Sayan Ranu. 2017. Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study. In SIGMOD. ACM 651--666.
3. Suman Banerjee Mamata Jenamani and Dilip Kumar Pratihar. 2020. A survey on influence maximization in a social network. Knowl. Inf. Syst. (2020) 3417--3455.
4. A fractional memory-efficient approach for online continuous-time influence maximization
5. Shishir Bharathi, David Kempe, and Mahyar Salek. 2007. Competitive Influence Maximization in Social Networks. In Internet and Network Economics, Third International Workshop, WINE (Lecture Notes in Computer Science, Vol. 4858). Springer, 306--311.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Optimizing Network Resilience via Vertex Anchoring;Proceedings of the ACM Web Conference 2024;2024-05-13