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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiang Zhang Dejun Yang Guoliang Xue |
| Copyright Year | 2014 |
| Description | Author affiliation: Colorado Sch. of Mines, Golden, CO, USA (Dejun Yang) || Arizona State Univ., Tempe, AZ, USA (Xiang Zhang; Guoliang Xue) |
| Abstract | In a competitive environment, competing agents would maximize their ideas' influence for higher profits. For example, in an unsaturated market, when a new company participates in the market sharing competition, it would distribute free tryout or discount to several customers, let them adopt the product or service, and influence others to use this product as propagation goes. This situation can also be applied to other scenarios, such as spreading new ideas in online social networks, political elections, and so on. In this paper, we use a model called Dynamic Influence in Competitive Environments (DICE) to perform the influence propagation. We first prove that finding the optimal utility for the new agent is an NP-hard problem under DICE. Then, we provide an algorithm for these new companies, and prove that the algorithm has a (1/3 - ϵ/n)-approximation ratio to the maximum payoff value. Performance results show that our algorithm has a better performance compared to existing strategies in terms of maximizing the utility for new agents. |
| Starting Page | 3932 |
| Ending Page | 3937 |
| File Size | 312153 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479920037 |
| DOI | 10.1109/ICC.2014.6883935 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Companies Algorithm design and analysis Computational modeling Greedy algorithms Topology Social network services Approximation algorithms |
| Content Type | Text |
| Resource Type | Article |
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