Name
Abstract | ||
|---|---|---|
Word-of-mouth plays an important role in the spread of influence by means of a cascading manner, e.g., the promotion of new products, information dissemination, etc. Today’s Online Social Networks (OSNs) provide viral marketing for many companies to conduct business promotion, and the idea behind viral marketing comes from the word-of-mouth effects. Given a predefined budget (e.g., the set size k), influence maximization selects a set of initial users to help companies to promote their products. The budget-making often is of challenging owning to the hardness of trade-off between profit and cost. Most of recent research on influence maximization primarily study the problem of independent product promotion, but few take into account the impacts of other existing products that users have already owned. Moreover, the current solutions encounter scalability problem whenever facing a large scale social network. In this paper, we explore the viral marketing by defining a general influence maximization problem where a practical scenario of coexistence of multiple products is considered. To capture the impacts of other existing products, we put forward a new method to measure the influence between users. Different from previous work, the goal of our general problem is to maximize the profit/cost ratio which can avoid the difficulty of budget-making and reflects maximization effects better. Then, we propose a (\(\frac {1}{2} + \varepsilon \))-approximate algorithm with linear complexity to solve the problem, and further design a distributed implementation to achieve good scalability. Extensive experiments on real world datasets validate the effectiveness and efficiency of our solution. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s11280-018-0651-5 | World Wide Web |
Keywords | Field | DocType |
Viral marketing, Influence maximization, Online social networks, Submodular maximization, Speedup | Viral marketing,Social network,Computer science,Operations research,Artificial intelligence,Linear complexity,Information Dissemination,Machine learning,Maximization,Scalability,Cost efficiency,Speedup | Journal |
Volume | Issue | ISSN |
22 | 6 | 1573-1413 |
Citations | PageRank | References |
2 | 0.36 | 24 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-Ya Zhou | 1 | 64 | 16.35 |
| Jianxi Fan | 2 | 718 | 60.15 |
| Jin Wang | 3 | 121 | 17.02 |
| Xi Wang | 4 | 85 | 6.56 |
| Lingzhi Li | 5 | 2 | 0.36 |