Name
Abstract | ||
|---|---|---|
In online social networks, many application problems can be generalized as influence maximization problem, which targets at finding the top-k influential users. Most of the existing influence spread models ignore user's attitude and interaction and cannot model the dynamic influence process. We propose a novel influence spread model called Fluidspread, using the fluid dynamics theory to reveal the time evolving influence spread process. In this paper, we model the influence spread process as the fluid update process in three dimensions: the fluid height difference, the fluid temperature and the temperature difference. To the best of our knowledge, this is first attempt of using the fluid dynamics theory in this field. Moreover, we formulate the Maximizing Positive Influenced Users (MPIU) problem and design the Fluidspread greedy algorithm to solve it. Through the experimental results, we demonstrate the effectiveness and efficiency of our Fluidspread model and Fluidspread greedy algorithm. (C) 2017 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.future.2017.05.050 | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE |
Keywords | Field | DocType |
Online social networks,Fluid dynamics,Influence maximization,Attitude,Diffusion model | Temperature difference,Mathematical optimization,Social network,Computer science,Greedy algorithm,Fluid dynamics,Maximization | Journal |
Volume | ISSN | Citations |
86 | 0167-739X | 3 |
PageRank | References | Authors |
0.39 | 27 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Wang | 1 | 10 | 2.83 |
| Wenjun Jiang | 2 | 356 | 24.25 |
| Xiao-Lin Li | 3 | 89 | 16.69 |
| Guojun Wang | 4 | 1740 | 144.41 |