scholarly journals A User Identification Algorithm Based on User Behavior Analysis in Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 47114-47123 ◽  
Author(s):  
Kaikai Deng ◽  
Ling Xing ◽  
Longshui Zheng ◽  
Honghai Wu ◽  
Ping Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Behavior Analysis ◽  
User Behavior ◽  
Identification Algorithm ◽  
User Identification ◽  
User Behavior Analysis

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

scholarly journals BANANA: when Behavior ANAlysis meets social Network Alignment

2020 ◽  
Author(s):  
Fuxin Ren ◽  
Zhongbao Zhang ◽  
Jiawei Zhang ◽  
Sen Su ◽  
Li Sun ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
User Behavior ◽  
Network Alignment ◽  
Learning Performance ◽  
User Behavior Analysis ◽  
The Social ◽  
End To End ◽  
Real World Datasets

Recently, aligning users among different social networks has received significant attention. However, most of the existing studies do not consider users’ behavior information during the aligning procedure and thus still suffer from the poor learning performance. In fact, we observe that social network alignment and behavior analysis can benefit from each other. Motivated by such an observation, we propose to jointly study the social network alignment problem and user behavior analysis problem. We design a novel end-to-end framework named BANANA. In this framework, to leverage behavior analysis for social network alignment at the distribution level, we design an earth mover’s distance based alignment model to fuse users’ behavior information for more comprehensive user representations. To further leverage social network alignment for behavior analysis, in turn, we design a temporal graph neural network model to fuse behavior information in different social networks based on the alignment result. Two models above can work together in an end-to-end manner. Through extensive experiments on real-world datasets, we demonstrate that our proposed approach outperforms the state-of-the-art methods in the social network alignment task and the user behavior analysis task, respectively.

Boost Clickbait Detection Based on User Behavior Analysis

2017 ◽  
pp. 73-80 ◽  
Author(s):  
Hai-Tao Zheng ◽  
Xin Yao ◽  
Yong Jiang ◽  
Shu-Tao Xia ◽  
Xi Xiao
Keyword(s):  
Behavior Analysis ◽  
User Behavior ◽  
User Behavior Analysis
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