Name
Abstract | ||
|---|---|---|
Vast quantities of data are generated by social networks in seconds. The information generated in a social network is transformed into a flow by the subjects who produce, transmit, and consume it. This flow can be represented in a very complicated directional graph where each subject is represented as a node, and the flow of information is represented as a directed edge. In this paper, we introduce a method of dividing this complex directional graph by user and quantifying the flow of information between and among users based on information flow vectors. We propose a system that can monitor the flow of information in social networks using information flow vectors extracted from social media data. We also introduce an improved skyline algorithm that can respond quickly to a user's various queries. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ACCESS.2018.2829495 | IEEE ACCESS |
Keywords | Field | DocType |
Information flow, social media data, skyline, Lambda architecture, MapReduce | Skyline,Data mining,Information flow (information theory),Social network,Social media,Division (mathematics),Monitoring system,Computer science,Flow (psychology),Big data,Distributed computing | Journal |
Volume | ISSN | Citations |
6 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sang Hun Han | 1 | 6 | 0.81 |
| Aziz Nasridinov | 2 | 49 | 14.32 |
| Keun Ho Ryu | 3 | 883 | 85.61 |