Research On Profit Maximization Of Social Network Based On Price Discount

With the rapid development of online social networks(OSN),information can quickly spread in social networks through word-of-mouth effects.The development of social networks has not only changed the way people communicate but also brought many new opportunities to Internet product marketing.This paper studies the profit maximization problem of social networks based on price discounts,including the profit maximization of the OSN platform and the profit maximization of the product company,which have more valuable in practical marketing solutions.The profit of the OSN platform comes from the commissions paid by advertisers according to the spread of influence.There have been many studies on profit maximization,but most of these methods do not consider the actual influence factors of influence spread,and the time complexity is high,which is difficult to apply to large-scale social networks.This paper combines the important factors of influence communication such as price discount,user valuation and user interest,and proposes a new influence communication model:the Independent Cascade with Price Discount(PDIC)model.Based on the PDIC model,in order to improve the efficiency of seed node selection,this paper further proposes a High-efficiency Greedy with Pruning(HGP)algorithm,which can reduce the search space and optimize the profit of the OSN platform.This paper tested the HGP algorithm on multiple real social network data sets.The experimental results verify that the algorithm can reduce the search space by more than 50%under the time complexity(9)),and its pre-selected seed node set must be included in the optimal Seed node set,which makes this algorithm closer to the optimal solution in terms of profit return than other algorithms.On some data,the profit result obtained by the HGP algorithm is more than 30%higher than that of the existing algorithm.At present,the time complexity of the existing seed node selection algorithm is(9)~2),and the time complexity of the HGP algorithm is(9)+6)~2),where6)is much smaller than9),which shows that the time complexity of the HGP algorithm is lower than The existing algorithms have high operating efficiency.For product companies,the profits they obtain are directly related to product prices and the number of product adoptions.Designing effective marketing strategies is crucial to obtaining more profits.However,most existing research only focuses on maximizing product influence,rather than explicitly incorporating pricing factors into the design of marketing strategies.This paper studies product marketing strategies and pricing models,and proposes a Two-stage Pricing with Discount(TPDM)Model.This model divides product marketing into two stages:the original price stage and the discount stage,and studies Advertising Marketing(AM)and Word-of-mouth Marketing(WM)on the number of products adopted.Based on the TPDM model,a Two-stage with Discount Greedy(TSDG)Algorithm is proposed to realize the product pricing of the product company.This paper uses several real social network data sets for comparative experiments.The experimental results show that the TSDG algorithm divides the product marketing into two stages and pricing can dig out the economic benefits of the product,so that more users can adopt the product.Compared with other algorithms,it can increase the profit of the product company by more than 20%,and it also has an advantage in running time.