Name
Title | ||
|---|---|---|
A key elements influence discovery scheme based on ternary association graph and representation learning |
Abstract | ||
|---|---|---|
Key elements refer to the elements that play a crucial role in disseminating information in social networks. The influence discovery of key elements can guide a series of works, such as public opinion control, user recommendation, and marketing promotion. Recently, there have been many studies on the influence of elements, but at present, many methods focus on either the influence discovery of key elements of different types or the dynamic influence discovery of a certain type of element alone, and rarely consider the combination of the two. Therefore, this study proposes a key element discovery algorithm based on a ternary association graph and representation learning, which can detect the influence of paths, users, and user groups. Additionally, the changes of different types of key elements can be analyzed according to the influence of elements in each stage of topic communication. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.knosys.2021.107359 | Knowledge-Based Systems |
Keywords | DocType | Volume |
Social networks,Hotspot topic,Key elements influence,Representation learning,Ternary association graph | Journal | 229 |
ISSN | Citations | PageRank |
0950-7051 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qian Li | 1 | 1 | 3.39 |
| Meiling Li | 2 | 0 | 0.34 |
| Xu Shi | 3 | 0 | 0.34 |
| Bin Wu | 4 | 290 | 52.43 |
| Yunpeng Xiao | 5 | 33 | 10.88 |